insects Article
Connecting the Dots: From an Easy Method to Computerized Species Determination Senta Niederegger 1, *, Klaus-Peter Döge 2 , Marcus Peter 2 , Tobias Eickhölter 2 and Gita Mall 1 1 2
*
Institute of Legal Medicine, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany;
[email protected] Department of Electrical Engineering and Information Technology, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany;
[email protected] (K.-P.D.);
[email protected] (M.P.);
[email protected] (T.E.) Correspondence:
[email protected]; Tel.: +49-3641-9397130
Academic Editors: David Rivers and John R. Wallace Received: 8 March 2017; Accepted: 12 May 2017; Published: 18 May 2017
Abstract: Differences in growth rate of forensically important dipteran larvae make species determination an essential requisite for an accurate estimation of time since colonization of the body. Interspecific morphological similarities, however, complicate species determination. Muscle attachment site (MAS) patterns on the inside of the cuticula of fly larvae are species specific and grow proportionally with the animal. The patterns can therefore be used for species identification, as well as age estimation in forensically important dipteran larvae. Additionally, in species where determination has proven to be difficult—even when employing genetic methods—this easy and cheap method can be successfully applied. The method was validated for a number of Calliphoridae, as well as Sarcophagidae; for Piophilidae species, however, the method proved to be inapt. The aim of this article is to assess the utility of the MAS method for applications in forensic entomology. Furthermore, the authors are currently engineering automation for pattern acquisition in order to expand the scope of the method. Automation is also required for the fast and reasonable application of MAS for species determination. Using filters on digital microscope pictures and cross-correlating them within their frequency range allows for a calculation of the correlation coefficients. Such pattern recognition permits an automatic comparison of one larva with a database of MAS reference patterns in order to find the correct, or at least the most likely, species. This facilitates species determination in immature stages of forensically important flies and economizes time investment, as rearing to adult flies will no longer be required. Keywords: forensic entomology; muscle attachment sites; species determination; image processing; correlation; knowledge base
1. Introduction Blowflies often start the biological clock for postmortem interval (PMI) calculation in forensic entomology. They might approach a body within the first hours after death [1–3] for oviposition. The age of the oldest blowfly found on a body or carcass is therefore a good estimate for the minimum postmortem interval or minimum exposure time [4–6]. Laboratory experiments growing larvae of known species under controlled temperature regimes showed that age correlates to the body length of the animals [7–11]. However, even though the larvae of different species look extremely similar, they might grow to different body lengths in the same time and under identical conditions [11]. Species and age determination can therefore be a great challenge when a conglomerate of unidentified larvae is collected from a body in the field.
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2017, 8, 52 2 of 16hatch, AInsects common method for species determination is to allow collected larvae to pupate and and to determine the species by morphological features of the imago [3,12]. This is very time-consuming A common method for species determination is to allow collected larvae to pupate and hatch, and requires living larvae. Other methods based on light microscopy rely on the morphological and to determine the species by morphological features of the imago [3,12]. This is very characteristics of the larvae, like the shape of the cephalopharyngeal skeleton (CPS), spiracles, or time-consuming and requires living larvae. Other methods based on light microscopy rely on the spinebands [13–22].characteristics These methods be like hindered by of the or discoloration morphological of thecan larvae, the shape thefragmentation cephalopharyngeal skeleton (CPS),of the specimen by poor preservation. Technically advanced approaches aimbytoward DNA-based or species spiracles, or spinebands [13–22]. These methods can be hindered the fragmentation identification using biological methods [23–25] or electron microscopy [26,27]. Requiring discoloration of molecular the specimen by poor preservation. Technically advanced approaches aim toward DNA-based identification using molecular [23–25] or electron cost-intensive andspecies specialized equipment, these methodsbiological might notmethods be applicable in every laboratory.
microscopy [26,27]. Requiring cost-intensive and specialized equipment, these methods might not be
2. Approach from the Biological applicable in every laboratory. Perspective A2. fast and easy suited for all larval instars and even applicable to a fragmented or Approach from method, the Biological Perspective discolored specimen, was developed in 2012: While attempting to locate trichoid sensilla, round A fast and easy method, suited for all larval instars and even applicable to a fragmented or structures were detected on the inside of the cuticula of a number of brachyceran larvae. Further discolored specimen, was developed in 2012: While attempting to locate trichoid sensilla, round investigation revealed that they were attachment sites for wall muscles. The thoracic structures were detected on the inside of the cuticula of transversal a number ofbody brachyceran larvae. Further segments, as well as the firstthat andthey last were abdominal segments, eachtransversal possess a body unique muscle arrangement, investigation revealed attachment sites for wall muscles. The while thoracic the remaining abdominal segments share an identical pattern [28,29]. The body wall segments, as well as the first and last abdominal segments, each possess a unique musculature muscle arrangement, while the remaining abdominal segments share an identical pattern [28,29].1a). TheLarval body flies of many larvae is composed of longitudinal, diagonal, and transversal muscles (Figure wall musculature of many is composed longitudinal, diagonal, and transversal muscles lack circumferential muscles as larvae antagonists to the of longitudinal muscles [30]. The vertical displacement 1a). Larval flies lack circumferential muscles as antagonists the longitudinal muscles [30].in the of the(Figure segments is therefore probably supported by the transversaltomuscles which are located The vertical displacement of the segments is therefore probably supported by the transversal external layer, where they form “blocks” in each segment and are directly attached to the cuticula [31] muscles which are located in the external layer, where they form “blocks” in each segment and are (Figure 1b). directly attached to the cuticula [31] (Figure 1b).
Figure 1. (a) Opened larva with complete musculature; (b) The longitudinal muscles were removed
Figureto1.reveal (a) Opened larva with complete musculature; (b) The longitudinal muscles were removed to the underlying transversal muscle groups. reveal the underlying transversal muscle groups. 2.1. Species and Age Determination
2.1. SpeciesThe andmuscle Age Determination attachment sites (MAS) of these transversal muscles could easily be visualized by removing the attachment muscles fromsites the cuticle and staining with Commassie The The muscle (MAS) of subsequent these transversal muscles couldbrilliant easily blue. be visualized analysis of MAS revealed that the structures are arranged in rows and form distinct patterns (Figure 2a). by removing the muscles from the cuticle and subsequent staining with Commassie brilliant blue. Ten larvae of each species were prepared in this fashion and the pictures were recorded. A small number The analysis of MAS revealed that the structures are arranged in rows and form distinct patterns of row subsets located at the center of the last two thoracic and the first abdominal segment (Segments 2– (Figure 2a). Ten larvae of each species were prepared in and thiscondensed fashion and were recorded. 4) were manually charted. Correlating rows were stacked into the rowpictures patterns (Figure 2b) A small row subsets located at the center last two thoracic and the first abdominal andnumber describedoffor a few forensically important speciesofofthe blowflies (Diptera: Calliphoridae) Calliphora segment (Segments were manually charted.terraenovae Correlating rows were regina stacked condensed vomitoria, C. vicina,2–4) Lucilia sericata [32], Protophormia [33], and Phormia [34].and The results confirmed interspecific variation which allowed for the species differentiation of these five species. into row patterns (Figure 2b) and described for a few forensically important species ofThere blowflies can be variation in the number of attachment a row[32], in larvae of the same age and [33], (Diptera: Calliphoridae) Calliphora vomitoria, C. points vicina,composing Lucilia sericata Protophormia terraenovae and Phormia regina [34]. The results confirmed interspecific variation which allowed for the species differentiation of these five species. There can be variation in the number of attachment points
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composing a row Insects 2017, 8, 52 in larvae of the same age and species. Such variability is common in 3biological of 16 systems and also present in other morphological characteristics, such as the ratio of the length of the species. is common in biological systems and tip also[35] present in length other morphological Insects 2017,Such 8, 52 variability 3 of 16 mouth hook base and the distance between the base and the or the and width of the characteristics, such as the ratio of the length of the mouth hook base and the distance between the base posterior spiracle discs [36]. The reason for the divergence in MAS numbers remains unclear, but does species. variability is common systems and also present in other morphological and the Such tip [35] or the length and widthinofbiological the posterior spiracle discs [36]. The reason for the divergence not affect the usability of the method. characteristics, such as the unclear, ratio of the of affect the mouth hook base and the distance between the base in MAS numbers remains butlength does not the usability of the method. and the tip [35] or the length and width of the posterior spiracle discs [36]. The reason for the divergence in MAS numbers remains unclear, but does not affect the usability of the method.
Figure 2. (a) All muscles were removed from the transparent cuticle, and attachment sites were
Figure 2. (a)with AllCoomassie muscles were removed fromare thelaterally transparent cuticle, and attachment sites were colored brilliant blue. Patterns symmetrical. Investigated muscle groups colored with 2A, Coomassie brilliant Patterns are laterally symmetrical. Investigated muscle groups labelled 3A, 3B, and 4A, 4B,blue. according to segments; (b) Patterns of ten larvae of the same species Figure 2. (a) All muscles were removed from the transparent cuticle, and attachment sites were labelled 2A, 3A, 3B, with and 4A, 4B, according to segments; (b) Patterns ofdots. ten larvae of the same species photographed, attachment sites marked with semitransparent Resulting rows were colored with Coomassie brilliant blue. Patterns are laterally symmetrical. Investigated muscle groups stacked andwith areasattachment with a highsites degree of overlap marked in black.dots. Resulting rows were stacked photographed, marked withare semitransparent labelled 2A, 3A, 3B, and 4A, 4B, according to segments; (b) Patterns of ten larvae of the same species and areas with a high degree of overlap are marked in semitransparent black. photographed, with attachment sites marked with dots. Resulting rows were The observation of larvae in different developmental stages further showed that the basic stacked and areas with a high degree of overlap are marked in black. central muscle attachment site patterns are conserved throughout development. The length of each The of larvae inthe different developmental stages further showed that thethus basic central row,observation however, increases with body length of the maggot (Figure 3). The method could not The observation of larvaeare in different developmental stages further showed that the basic row, muscle attachment site patterns conserved throughout development. The length of each only be used for species determination, but also for age determination [33]. central muscle attachment site patterns are conserved throughout development. The length of each however, increases with the body length of the maggot (Figure 3). The method could thus not only be row, however, increases with the body length of the maggot (Figure 3). The method could thus not used for species determination, but also for age determination [33]. only be used for species determination, but also for age determination [33].
Figure 3. Pattern consistency during growth of C. vomitoria.
Figure 3. Pattern consistency during growth of C. vomitoria.
Figure 3. Pattern consistency during growth of C. vomitoria.
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2017,Lucilia 8, 52 2.2.Insects Genus
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2.2. Genus Lucilia further challenge the method, six closely related and forensically relevant species of the genus 2.2.To Genus Lucilia To further challenge the method, six closely related and forensically relevant species of the Lucilia were tested [37]. In order to achieve reliable results, the set of analyzed MAS patterns had genus tested [37]. In order six to achieve the set of analyzed MAS patterns ToLucilia furtherwere challenge the method, closely reliable related results, and forensically relevant species of the to be enlarged. While isolated central patterns yielded adequate results in the first experiments [32], had toLucilia be enlarged. While[37]. isolated central patternsreliable yieldedresults, adequate in the first experiments genus were tested In order to achieve theresults set of analyzed MAS patterns it became necessary to include all rows present on segments two, three, and four. The labeling of MAS [32],to it be became necessary includecentral all rows present on segments two, three,inand The labeling had enlarged. Whiletoisolated patterns yielded adequate results thefour. first experiments rows was changed accordingly (Figure 4, shown for Calliphora vicina). of MAS rows was changed accordingly (Figure 4, shown for Calliphora vicina). [32], it became necessary to include all rows present on segments two, three, and four. The labeling of MAS rows was changed accordingly (Figure 4, shown for Calliphora vicina).
Figure 4. New labeling in segment 4 shown for Calliphora vicina. Figure 4. New labeling in segment 4 shown for Calliphora vicina. Figure 4. New labeling in segment 4 shown for Calliphora vicina.
A joint genus pattern (Figure 5) could be found by combining pictures of all six Lucilia species AA joint genus (Figure 5) be found by combining pictures all Lucilia species tested (L. ampullacea, L. caesar, L. illustris, L. richardsi, sericata, and L.pictures silvarum). Each species differed joint genuspattern pattern (Figure 5) could could foundL.by combining ofofall sixsix Lucilia species tested ampullacea, L.L.caesar, L.L.illustris, richardsi, L.allowed sericata,for and silvarum). Each species differed from(L. the genus pattern in a characteristic ThisL. the composition ofspecies distinct species tested (L. ampullacea, caesar, illustris, L. L. way. sericata, and L.L.silvarum). Each differed patterns for each of theinin six species. For L. way. illustris and L. caesar—where the speciesofdetermination of from genus pattern characteristic This allowed for distinct species from thethe genus pattern aacharacteristic This allowed forthe thecomposition composition of distinct species larvae was reported to be difficult [38–40] or impossible [41], even with genetic methods [42]—MAS patterns eachofofthe thesix sixspecies. species.For For L. L. illustris illustris and determination of of patterns forfor each and L. L. caesar—where caesar—wherethe thespecies species determination patterns were distinctive [37]. larvae was reported bedifficult difficult[38–40] [38–40] or or impossible impossible [41], [42]—MAS larvae was reported totobe [41],even evenwith withgenetic geneticmethods methods [42]—MAS patterns were distinctive[37]. [37]. patterns were distinctive
Figure 5. Genus pattern for Lucilia composed of 76 individual MAS patterns. Figure5.5.Genus Genuspattern pattern for for Lucilia composed Figure composedof of76 76individual individualMAS MASpatterns. patterns.
2.3. Genus Sarcophaga 2.3. Genus Sarcophaga 2.3. Genus Sarcophaga Flesh fly (Sarcophaginae) larvae, including the genus Sarcophaga, can easily be recognized from the large and deep spiracular cavity on the last segment [43]. Species identification, however, is Flesh fly (Sarcophaginae) larvae, including thegenus genus Sarcophaga, caneasily easilybe berecognized recognized from Flesh fly (Sarcophaginae) larvae, including the Sarcophaga, can from the much more difficult. Therefore,cavity the next in the development of theidentification, method was designed the large and deep spiracular on step the last segment [43]. Species however,for is large and deep spiracular cavity on the last segment [43]. Species identification, however, is much more five species of the Therefore, genus Sarcophaga from five development different subgenera. Sarcophaga albiceps, for S. much more difficult. the next step in the of the method was designed difficult. Therefore, the next step in the development of the method was designed for five species of argyrostoma, melanura, and S. similis previously foundSarcophaga to be frequent visitors five species S.ofcaerulescens, the genusS. Sarcophaga from five were different subgenera. albiceps, S. the genus Sarcophaga from five different subgenera. Sarcophaga albiceps, S. argyrostoma, S. caerulescens, and successful colonizers ofS.large animal carrion andwere human corpses [44–50]. the sixvisitors Lucilia argyrostoma, S. caerulescens, melanura, and S. similis previously found toAs befor frequent S. melanura, and S. similis were previously found to be frequent visitors and successful colonizers of species, a genus pattern of could generated from fivecorpses Sarcophaga species 6) and and successful colonizers largebeanimal carrion andthese human [44–50]. As for(Figure the six Lucilia large animal carrion and human corpses [44–50]. As for the six Lucilia species, a genus pattern could species-specific found [51]. previously used set of MAS did not have to species, a genuspattern patternvariations could bewere generated fromThe these five Sarcophaga species (Figure 6) and bespecies-specific generated from these variations five Sarcophaga species 6) and species-specific pattern were found [51].(Figure The previously used set of MASpattern did not variations have to
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be extended. The genus pattern for Sarcophaga species is distinctively different from the Lucilia were found [51].The The previously used of of MAS did notbetween have to genera be extended. Thefrom genus pattern genus pattern. This indicates the presence variations and families which promise be extended. genus pattern for set Sarcophaga species is distinctively different the Luciliafor to be very useful fordistinctively the MASthe method. Sarcophaga species is different the Lucilia genusgenera pattern. This indicates thepromise presence genus pattern. This indicates presence from of variations between and families which to be very useful for genera the MAS method. of variations between and families which promise to be very useful for the MAS method.
Figure 6. Genus pattern for Sarcophaga composed of 50 individual MAS patterns. Figure 6. Genus pattern for Sarcophaga composed of 50 individual MAS patterns. Figure 6. Genus pattern for Sarcophaga composed of 50 individual MAS patterns.
2.4. Family Piophilini 2.4.2.4. Family Piophilini Family Piophilini Preliminary analyses of Piophilini species showed that their muscle equipment is clearly Preliminary of Piophilini showed that their muscle equipment isspecies clearly different different fromanalyses theanalyses Calliphoridae andspecies Sarcophagidae (Figure The muscle very similar Preliminary of Piophilini species showed that7). their equipment is patterns clearly from the Calliphoridae and Sarcophagidae (Figure 7). The very similar species patterns observed observedfrom between the two closelyand related Piophilina (Figure genera and a more related genus of different the Calliphoridae Sarcophagidae 7). The verydistantly similar species patterns between the between two closely Piophilina genera a genera more distantly related genus of Thyreophorina, Thyreophorina, however, that the larvaland MAS patterns may be highly conserved among observed therelated twosuggest closely related Piophilina and a more distantly related genusthe of however, suggest that the larval MAS patterns may be highly conserved group [52]. group [52]. Sufficiently conspicuous variations reliable species differentiation werethe not visiblethe to Thyreophorina, however, suggest that the larvalfor MAS patterns may be highly among conserved among the naked eye. Sufficiently conspicuous variations for reliable species differentiation were not visible to the naked eye. group [52]. Sufficiently conspicuous variations for reliable species differentiation were not visible to the naked eye.
Figure Preliminaryfamily familypattern pattern for for Piophilini. Piophilini. Figure 7.7.Preliminary Figure 7. Preliminary family pattern for Piophilini.
Next Steps 2.5.2.5. Next Steps 2.5. Next Steps main aim methodisisfast fastand andreliable reliable species species identification larvae. TheThe main aim of of thethe method identificationin insingle, single,unknown unknown larvae. A vast pool of data, as well as reference genus and species patterns, is an essential requirement for The main aim of the method is fast and reliable species identification in single, unknown larvae. A vast pool of data, as well as reference genus and species patterns, is an essential requirement for comparison. is therefore importance to optimize the process pattern requirement acquisition. The A vast pool ofItdata, as well of as vital reference genus and species patterns, is anofessential for comparison. It is therefore of vital importance to optimize the process of pattern acquisition. The manual manual charting patternsof is vital very time consuming. The manual generation of reference patternsThe for comparison. It is of therefore importance to optimize the process of pattern acquisition. charting of patterns is very time consuming. The manual generation of reference patterns for genera genera and species can never be objective. An automation of thegeneration process is of therefore required manual charting of patterns is very time consuming. The manual reference patternsfor fora and species can never be objective. An automation of the process is therefore required for a successful successful the be knowledge of the method. It couldis furthermore permitforthe genera and extension species canofnever objective. base An automation of the process therefore required a extension of the knowledge base of the method. It could furthermore permit the distinction of patterns distinction of patterns with only marginal differences. A large data base, on the other hand, is reliant successful extension of the knowledge base of the method. It could furthermore permit the with only marginal differences. A large data base, on the other hand, is reliant on efficient comparison distinction of patterns with only marginal differences. A large data base, on the other hand, is reliant methods to achieve useful results. The comparison of patterns for species determination necessitates
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on efficient comparison methods to achieve useful results. The comparison of patterns for species adetermination degree of expertise and using an extensive database be very time-consuming. Customized necessitates a degree of expertise and would using an extensive database would be very software is therefore the only option to allow for a widespread and successful application of and the time-consuming. Customized software is therefore the only option to allow for a widespread MAS method. successful application of the MAS method. 3. Problem Analysis Analysis from from the the Image 3. Problem Image Processing Processing Perspective Perspective In image processing processing will will be be examined examined as as aa potential potential method method of of refinement. refinement. In the the following, following, image Digital hashas continuously developed overover the last However, for this method, Digital image imageprocessing processing continuously developed thedecades. last decades. However, for this it was necessary to test the general applicability before process development. This is due to method, it was necessary to test the general applicability before process development. mainly This is mainly the of image and wasand done indone a series example images of different species. duevariability to the variability of information image information was in aofseries of example images of different Images of larvae belonging to instar L3 were exclusively used for all of the following tests. species. Images of larvae belonging to instar L3 were exclusively used for all of the following tests. The showed that that patterns patterns within within the the same same segments segments of of aa species species are are recognizable, recognizable, The evaluation evaluation showed but Neither the the number number of of spots spots nor nor the the general general shape shape is is predictable, predictable, which which is is illustrated illustrated by by but variable. variable. Neither the rectangle in Figure 8. For a human observer, the differentiation of species is still possible [32,37]. the rectangle in Figure 8. For a human observer, the differentiation of species is still possible [32,37]. For an automatized automatized procedure, procedure,variability variabilitypresents presentsa achallenge challenge due missing contextualization. For an due to to missing contextualization. In In addition to the variability of patterns, differences in the sizes of larvae need to be taken into account addition to the variability of patterns, differences in the sizes of larvae need to be taken into account in in automated automated processes. processes.
Figure 8. Examples of image information variability in the fourth segment of Lucilia ampullacea. Figure 8. Examples of image information variability in the fourth segment of Lucilia ampullacea. Variability of individual patterns (rectangle), reflections (circle), and variation of resolutions (arrow) Variability of individual patterns (rectangle), reflections (circle), and variation of resolutions (arrow) are shown. are shown.
Furthermore, transforming three-dimensional objects into two-dimensional images leads to Furthermore, transformingwhich three-dimensional into in two-dimensional images leads to differences in imagesharpness, is indicated byobjects the arrow Figure 8. The image also shows differences in imagesharpness, which is indicated by the arrow in Figure 8. The image also shows significant differences in brightness, highlighted by the circle in Figure 8, which are not immanent to significant brightness, highlighted by the circle Figure eye 8, which are not immanent to the the object differences itself, but in due to bad illumination. Again, theinhuman recognizes these effects as object itself, but due to bad illumination. Again, the human eye recognizes these effects as interferences interferences and is still able to deduce the underlying information. In an automated process, and is still these able toeffects deduce underlying need information. In an automated process, however, these effects however, ofthe interferences to be explicitly addressed. Consequently, for reliable of interferences need to be explicitly addressed. Consequently, for reliable capturing, it must be image capturing, it must be adjusted to fit the analytical process. Thisimage also includes the parallel adjusted to fit the analytical process. This also includes the parallel alignment of the pictorial object to alignment of the pictorial object to the vertical image edge. the vertical image edge.of expert knowledge for the identification of relevant patterns, as well as for Noticing the need Noticing the of expert knowledge for of theaidentification of relevant patterns,for as well as the elimination ofneed interferences, the utilization knowledge base was inevitable species for the elimination of interferences, the utilization of a knowledge base was inevitable for species determination in this project. determination in this project. Table 1 summarizes the postulated assessment of starting conditions. Table 1 summarizes the postulated assessment of starting conditions. Table 1. Pro and contra for automatised image processing application. Table 1. Pro and contra for automatised image processing application. Aspects Favouring Automatized Image Processing Challenges - definedFavouring illumination conditions Image Processing -Challenges different object sizes Aspects Automatized - simple patterns - differences in alignment - defined illumination conditions - different object sizes - easy acquisition of objects for knowledge base - differences due to biological variability - simple patterns - differences in alignment - easy acquisition of objects for knowledge base - differences due to biological variability
It appears worthwhile to enhance the task of species determination using methods of image processing. From the applications point of view, the task is to assign a three-dimensional object, the
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It appears worthwhile to enhance the task of species determination using methods of image processing. the applications point of view, the task is to assign a three-dimensional InsectsFrom 7 of 16 7 ofobject, Insects 2017, 8,2017, 52 8, 52 16 the larva, to its class hierarchy, consisting of family, genus, and species. Biological variability and larva, to its class hierarchy, consisting of family, genus, and species. Biological variability and larva, to its class hierarchy, consisting of taken family,into genus, and species. Biological interferences of image processing need to be consideration (Figure 9). variability and interferences of image processing need to be taken into consideration (Figure 9).
interferences of image processing need to be taken into consideration (Figure 9). Insects 2017, 8, 52
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larva, to its class hierarchy, consisting of family, genus, and species. Biological variability and interferences of image processing need to be taken into consideration (Figure 9).
Figure 9. Specification of problem definition and primary influential factors.
Figure 9. Specification of problem definition and primary influential factors. Figure 9. Specification 4. The Image Processing Softwareof problem definition and primary influential factors.
4. The ImageBuilding Processing on theSoftware generally applicable hierarchy of image processing operations [53], the steps 4. The Image Processing Software
shown in Figure 10 are necessary to solve the task using methods of image processing.
9. Specification of problem definition primary influential factors. Building on theFigure generally applicable hierarchy ofand image processing operations [53], the steps Building on the generally applicable hierarchy of image processing operations [53], the steps shown in Figure 10 10 areare necessary methodsofofimage image processing. shown in Figure necessarytotosolve solvethe the task task using using methods processing.
4. The Image Processing Software
Building on the generally applicable hierarchy of image processing operations [53], the steps shown in Figure 10 are necessary to solve the task using methods of image processing.
Figure 10. Solution from an image processing perspective.
4.1. Filtration The aim of filtration processes (Figure 11) is to emphasize the patterns which are to be used for classification and to Figure remove10. any image perturbations. In other words, interfering signals should be Solution from an an image Figure 10. Solution from imageprocessing processingperspective. perspective. suppressed. Starting with the original image (a), three steps are necessary to achieve this goal: 10.filtration Solution from an image processing perspective. high-pass filtration (b),Figure median as preparation for the generation of a binary image (c). 4.1. Filtration
4.1. Filtration
4.1. Filtration The aim of filtration processes (Figure 11) is to emphasize the patterns which are to be used for The aim of and filtration processes (Figure 11) is to emphasize the patterns which are to bebeused classification to remove any image perturbations. In otherthe words, interfering signals should The aim of filtration processes (Figure 11) is to emphasize patterns which are to be used for for classification and to remove any image perturbations. In other words, interfering signals should suppressed. Starting theany original (a), three stepswords, are necessary tosignals achieve this be goal: classification and to with remove image image perturbations. In other interfering should be suppressed. Starting thefiltration originalas image three steps necessary to achieve this goal: high-pass filtration (b),with median preparation for the generation of atobinary image suppressed. Starting with the original image (a),(a), three steps are are necessary achieve this (c). goal:
high-pass filtration (b), median filtration forthe thegeneration generation a binary image high-pass filtration (b), median filtrationasaspreparation preparation for of aof binary image (c). (c).
Figure 11. Steps of the filtration process: Original image (a), high-pass filtration (b) and binary image (c).
Figure 11. Steps of the filtration process: Original image (a), high-pass filtration (b) and binary image (c). Figure 11. Steps of the filtration process: Original image (a), high-pass filtration (b) and binary image (c).
Figure 11. Steps of the filtration process: Original image (a); high-pass filtration (b); and binary image (c).
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4.1.1. High-Pass Filtration to Emphasize the Pattern The prerequisite for automated identification is a good perceptibility of the patterns against the background. Good visibility is necessary to obtain the characteristics of the desired signal. The light/dark boundary of the image allows for the separation of frequencies. In image processing, these boundaries are called “edges”. The image undergoing the filtration process is considered to be a two-dimensional signal. The edges correspond to high frequencies, which need to be emphasized. In signal processing, this can be achieved using high-pass filtration. A Sobel filter was chosen for the present application. This differentiation filter of the 1st order implements an approximation of the first derivative, and smoothing of the image in the orthogonal direction to the derivative. The filter operation consists of 3 × 3 matrices for the x and y direction.
−1 −2 −1 −1 0 1 S x = −2 0 2 , Sy = 0 0 0 −1 0 1 1 2 1 The filtration involves convolution of the image matrix A with the operators Sx and Sy , and results in the filtered image composed of the sum of the individual convolution results. Axy =
q
(S x ∗ A )2 + Sy ∗ A
2
4.1.2. Median Filtration to Reduce Image Noise The high-pass filtration emphasized the desired signal. Since the noise in the image also consists of high frequency sections, they, too, are now emphasized. They must therefore be suppressed separately using a median filter. The median for each pixel with its adjacent neighbor is calculated, resulting in nine values. The median value in this list represents the new brightness of the filtered pixel. At the image margin, the number of neighbors is reduced to five, and at the corner, to three. 4.1.3. Generating a Binary Image to Compare with the Knowledge Base The knowledge base consists of binary images. The patterns that are to be compared must therefore be converted into the same format. After the application of steps 1 and 2, the image still includes 256 brightness levels. The application of a static or dynamic threshold allows for the conversion of these brightness levels into binary values. A static threshold with a dynamic component was used in this case. The dynamic component is derived from the average brightness of all pixels. The static component is given by an offset value, which was set to an initial value of 20. This is a proper value for well exposed and sharp images and was determined empirically. 4.2. Area Identification The subdivision of individual patterns from the filtered image is currently done semi-automatically. The user identifies the relevant areas by manually drawing the outline as free form. Each selection containing a single pattern is then automatically stored as an individual image and labelled accordingly. The resulting images are further filtered for disruptive pixel groups, removing small groups of pixels that could negatively influence classification. The last step of the process is the automatic cropping and scaling of the pattern images to 100 pixels in height. This adjustment takes account of size differences in larvae according to species and the state of development. Area identification generates up to nine images per segment. For each animal, the images of all segments analyzed are stored in one extensible-markup-language-file (XML-file), sorted according to the location of the pattern in each segment. These files provide the base for the following classification tasks and enable visual verification of the image processing steps.
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4.3. Classification via Knowledge Base The classification of the patterns requires the generation and application of a knowledge base. Sample images of larvae belonging to 14 distinct species (Table 2) were selected and run through the steps described in Section 4.1 (Filtration). To achieve a high level of resolution, the smallest possible meaningful elements, i.e., the individual patterns (as shown in Figure 12 and previously explained in Figure 8), were chosen to construct the knowledge base. Table 2. Results of species determination. The results are listed alphabetically and given as a ratio of correctly identified larvae to the total number of larvae analyzed. The highlighted fields indicate trials Insects 2017, 8, 52 9 of 16 in which not all larvae were identified correctly. Trials marked with a dash (/) could not be executed 4.3. Classification via Knowledge Base due to the lack of suitable images of the required quality. No. 01 02 03 04 05 06 07 08 09 10 11 12 13 14
The classification of the patterns requires the generation and application of a knowledge base. Sample images of larvae belonging to 14 distinct species (Table 2) were selected and run through the Group Group B steps describedSpecies in Section 4.1 (Filtration). To achieve a high A level of resolution, the smallest possible meaningful elements, i.e., the individual patterns (as shown in Figure 12 and previously explained Trial base. Result Trial Result in Figure 8), were chosen to construct the knowledge
Calliphora vicina
A1
14/15
B1
14/15 4/7 6/8 7/7 B5Group B 4/4 B6 10/10 Trial Result B1B7 14/15 7/7 B2 4/7 B8 9/10 B3 6/8 B4B9 7/7 5/6 B5 4/4 7/7 B10 B6 10/10 B11 6/6 B7 7/7 B12 B8 9/10 3/3 B9 5/6 9/10 B13 B10 7/7 B14 B11 6/6 12/12
Table 2. Results of species determination. The/ results are listed alphabetically and given B2 as a ratio of Lucilia ampullacea / correctly identified larvae to the total number of larvae analyzed. The highlighted fields indicate Lucilia illustris / / B3 trials in which not all larvae were identified correctly. Trials marked with a dash (/) could not be Lucilia / required quality./ B4 executed due torichardsi the lack of suitable images of the No. 01 02 03 04 05 06 07 08 09 10 11 12 13 14
Lucilia silvarum Species Sarcophaga albiceps vicina SarcophagaCalliphora argyrostoma Lucilia ampullacea Sarcophaga caerulescens Lucilia illustris Sarcophaga Luciliasimilis richardsi Lucilia silvarum Wohlfarthia bella Sarcophaga albiceps Wohlfarthia indigens Sarcophaga argyrostoma Wohlfarthia Sarcophaganuba caerulescens Sarcophaga similis Wohlfarthia trina Wohlfarthia bella Wohlfarthia villeneuvi Wohlfarthia indigens Wohlfarthia nuba Wohlfarthia trina Wohlfarthia villeneuvi
/ / Group A / Trial / Result 14/15 / A1 / / // /// / / // / A10 / 3/3 / / A11 / 2/2 / / / // / A13 / 2/2 A10 3/3 / A11 /2/2 / A13 /
/ 2/2 /
B12 B13 B14
3/3 9/10 12/12
Reference patterns for each species are generated by user controlled iterative layering of the Reference patterns12). for each species are generated by user controlled iterative layering of the resulting sample images (Figure resulting sample images (Figure 12).
Figure 12. Layering of several individual patterns (a) to generate a reference pattern (b).
Figure 12. Layering of several individual patterns (a) to generate a reference pattern (b). The user is able to remove distorting images or to manually correct images. Such interventions are needed, especially when the patterns are heavily twisted or have an unusual size. The construction of theto reference patterns is therefore strongly or influenced by the experience knowledgeSuch of theinterventions are The user is able remove distorting images to manually correctandimages. person conducting the process. Consequently, the knowledge base represents expert know-how. needed, especially when thetopatterns heavily ortohave an unusual size. The construction of In analogy the analysisare images, referencetwisted images need be cropped and scaled to 100 pixels in height and saved as XML-files.influenced These files constitute the base for the the reference patterns is therefore strongly by the experience andcomputational knowledge of the person implementation of the knowledge base.
conducting the process. Consequently, the knowledge base represents expert know-how. In analogy to the analysis images, reference images need to be cropped and scaled to 100 pixels in height and saved as XML-files. These files constitute the base for the computational implementation of the knowledge base. To ultimately be able to perform species determination in images from unknown larvae, the classification method needs to be detailed. The calculation of a normative cross-correlation
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ultimately be able to perform species determination in images from unknown larvae, the Insects 2017,To 8, 52 10 of 16 classification method needs to be detailed. The calculation of a normative cross-correlation coefficient [54] between the patterns of the binary image Axy and the reference object Hxy is the basis coefficient [54] betweenused the patterns of the binary image Axy and the reference object Hxy is the basis of of the classification here.
the classification used here.
,
=
∑
,
,
+ ,
+
+ i,, y + + j) ∑ j,i Hxy, (i, j)Axy ( x + q k( x, y) = q is determined for each pattern, resulting in a characteristic gradient for each reference Hxy (i, j)2 Axy ( x + i, y + j)2
kmax image (Figure 13). average is calculated based onresulting this gradient and used for evaluation. The reference image kmaxAn is determined for each pattern, in a characteristic gradient for each reference image with the highest average correlation to the analysis image represents the fly species that the larva in (Figure 13). question supposedly belongs to.
Figure 13. Graphs of correlation for two patterns. Figure 13. Graphs of correlation for two patterns.
5. Results An average is calculated based on this gradient and used for evaluation. The reference image with the highest average correlation to the14analysis image represents species that The original images of larvae from known species were sortedthe intofly three groups forthe thelarva test in question supposedly belongs run of the software. Groupto.A consisted of high quality images containing a full set of patterns. Group B contained acceptable images missing single patterns. Group C contained images of poor
5. Results quality in which a large share of the patterns could not be extracted for reasons of poor lighting,
insufficient contrast, The original imagesor ofdiscolorations. larvae from 14 known species were sorted into three groups for the test run The images from group A andofB high were quality used to compile species, where further of the software. Group A consisted images references containingfora each full set of patterns. Group B differentiations were made: Class A references were exclusively composed of group A images. If the contained acceptable images missing single patterns. Group C contained images of poor quality in number of A images was insufficient for the generation of reliable reference patterns, class B which a large share of the patterns could not be extracted for reasons of poor lighting, insufficient references were composed either of group B images or a mix of A and B images. If necessary, C contrast, or discolorations. references were composed of B and C images. The While imagesgenerating from group and B were used to compile references for each species, where due further the A knowledge base, single images had to be removed from the layering differentiations made: Class A references weremelanura exclusively of group If the to significantwere contortion. Two species (Sacrophaga and composed Lucilia sericata) had toAbeimages. excluded number of A images insufficient for images. the generation reliable reference patterns, class B references altogether due towas the lack of suitable Patternsofwere visible, but could only be extracted in werepoor composed group images or agroup mix ofAAand and images. If necessary, were quality either or not of at all. For B some species, BB could be employed, and C forreferences others, only group B The test compared each image to each reference. composed ofcould B andbeCused. images.
While generating the knowledge base, single images had to be removed from the layering due 5.1. Resultscontortion. of Species Determination to significant Two species (Sacrophaga melanura and Lucilia sericata) had to be excluded altogetherThe duethree to the lack of suitable images. visible, coefficients, but could only be extracted in poor references with the highestPatterns averagewere correlation as well as the three references thesome highest sumgroups of correlation coefficients, were and selected as theonly results. quality or not atwith all. For species, A and B could be employed, for others, group B total of The 134 larvae from 14 each different species were analyzed, of which 124 (92.5%) could be couldAbe used. test compared image to each reference. identified correctly (Table 2).
5.1. Results of Species Determination The three references with the highest average correlation coefficients, as well as the three references with the highest sum of correlation coefficients, were selected as the results. A total of 134 larvae from 14 different species were analyzed, of which 124 (92.5%) could be identified correctly (Table 2).
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5.2. Results of Genus Determination The three references with the highest correlation coefficient are further investigated. The result is conceded as true if the three references belong to the same genus; otherwise, no genus could be determined. The test included 97 larvae from 13 different species, where 68 larvae (70%) were identified correctly (Table 3). Table 3. Results of genus determination. The results are listed alphabetically and given as a ratio of correctly identified larvae to the total number of larvae analyzed. The highlighted fields indicate trials in which not all larvae were identified correctly. Trials marked with a dash (/) could not be executed due to the lack of suitable images of the required quality. No. 01 02 03 04 05 06 07 08 09 10 11 12 13
Species Lucilia ampullacea Lucilia illustris Lucilia richardsi Lucilia silvarum Sarcophaga albiceps Sarcophaga argyrostoma Sarcophaga caerulescens Sarcophaga similis Wohlfarthia bella Wohlfarthia indigens Wohlfarthia nuba Wohlfarthia trina Wohlfarthia villeneuvi
Group A Trial / / / / / / / / A10 A11 / A13 /
Result / / / / / / / / 3/3 2/2 / 2/2 /
Group B Trial B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14
Result 6/7 8/8 3/7 3/4 6/10 1/7 0/10 3/6 7/7 6/6 3/3 10/10 12/12
One requirement is mandatory for genus determination: The references of the same genus must be more similar to one another than to those of any other genus. To validate this requirement, all references were compared to one another. For each reference, the three references with the highest correlation coefficients are given (extract in Table 4). Each genus was represented by at least four species. For a correct determination, all three correlating reference species must therefore be members of the same genus. Table 4. Similarities between the references (extract). The results are listed alphabetically; the numbers given are correlation coefficients compared to the reference. Highlighted fields indicate trials in which the genus of adjacent references differed from the genus of the reference analyzed. Reference Lucilia ampullacea Lucilia illustris Lucilia richardsi Lucilia silvarum Sarcophaga albiceps Sarcophaga argyrostoma Sarcophaga caerulescens Sarcophaga similis Wohlfarthia bella Wohlfarthia indigens Wohlfarthia nuba Wohlfarthia trina Wohlfarthia villeneuvi
References of Adjacent Similarity Lucilia richardsi (0.7944) Lucilia richardsi (0.7994) Lucilia ampullacea (0.8040) Lucilia richardsi (0.7950) Sarcophaga similis (0.7137) Sarcophaga caerulescens (0.7366) Sarcophaga similis (0.7612) Sarcophaga caerulescens (0.7714) Wohlfarthia villeneuvi (0.7671) Wohlfarthia bella (0.7402) Wohlfarthia indigens (0.7725) Wohlfarthia indigens (0.7833) Wohlfarthia indigens (0.7976)
Lucilia silvarum (0.7843) Lucilia silvarum (0.7882) Lucilia illustris (0.7930) Lucilia ampullacea (0.7890) Wohlfarthia indigens (0.7070) Sarcophaga similis (0.7305) Wohlfarthia trina (0.7598) Wohlfarthia indigens (0.7526) Wohlfarthia trina (0.7566) Wohlfarthia villeneuvi (0.7343) Wohlfarthia trina (0.7492) Wohlfarthia villeneuvi (0.7758) Wohlfarthia trina (0.7940)
Lucilia illustris (0.7759) Lucilia ampullacea (0.7872) Lucilia silvarum (0.7919) Lucilia illustris (0.7696) Sarcophaga caerulescens (0.7061) Lucilia richardsi (0.7243) Wohlfarthia villeneuvi (0.7489) Lucilia richardsi (0.7472) Wohlfarthia indigens (0.7382) Wohlfarthia trina (0.7341) Wohlfarthia villeneuvi (0.7402) Wohlfarthia nuba (0.7641) Wohlfarthia bella (0.7792)
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The highlighted fields indicate samples in which this could not be achieved. This suggests that differing genus references might display more similarities to the investigated sample than references of the corresponding genus. The identification of the genera Lucilia and Sacrophaga proved to be difficult. The genus Wohlfarthia, on the other hand, could always be determined correctly (Table 4). 5.3. Problem Analysis All of the following problems (Table 5) were identified during the process. The problems do not generally occur and combinations are possible. Table 5. Recognized problems and possible solutions. Problem
Solution
The patterns contain many holes. In holey patterns parts of the border may be missing. This affects the identification. Additionally, holey patterns may mislead interpretation of the layered image. The result is a reference pattern with poor properties.
The combination of high-pass filter and threshold should be adjusted. The aim is to recognise each pattern point as closed, hollow form. Afterwards a dilatation algorithm is applied.
The patterns of the reference are too voluminous. This changes the correlation coefficient.
Guidelines for the generation of references need to be set. Firstly, no pattern with holes should be used. Furthermore, it is important to layer at least 10 patterns, to generate a valid reference. Additionally, a new tool should be developed to allow the rotation of patterns.
In this case the pattern often has holes. This changes the correlation coefficient.
A predefined area can be built around the geometric centre of the pattern point. This new pattern can be used instead of the pattern point.
Misinterpretation of central patterns. The central pattern consists of two or more partial patterns. Those vary significantly in size and distance within one species. In addition, the patterns of species of the same genus show many similarities.
Three possible solutions could be investigated: Firstly, it should be tested whether the central pattern can be ignored. If this is not possible it should be scaled in width. If this also does not yield any favourable results, it might be tested whether the central pattern can be split along the axis of symmetry.
The pattern to be analysed is too voluminous.
6. Discussion and Outlook The aim of our work was to improve the method of MAS pattern recognition for species and age identification in larval instars of Diptera. The MAS method is based on the comparison of an unknown pattern (of a larva in question) with existing reference patterns (generated from patterns of a number of known larvae). A broad data base of reference patterns is needed for this task. Facilitation of MAS pattern recognition allows for the much faster generation of reference patterns and thus, the expansion of the knowledge base. At first glance, the use of other methods than a knowledge base sounds promising. Complex identification problems can often be solved with methods of so called artificial intelligence. This approach, however, had to be declined for the following reasons. First, it is hardly possible to create a suitable amount of learning data to meet the claim of, e.g., a neural network. This is due to the intricate process of the manual preparation of larvae. We are talking about at least 100 perfect samples to support the learning process. In the probabilistic case of failure or a reduced accuracy of the learning process, this number would have to be increased. The second reason is the basic necessity of a coefficient of determination. The proposed method provides the average correlation coefficient, while neural networks cannot evaluate the accuracy of the results of image processing by themselves. Entomological scientists from around the world might be needed to contribute and analyze a vast pool of known larvae for the MAS method. To facilitate this, protocols need to be established
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for standardization in the preparation of larvae, the production of original pictures, and the use of the software. All hitherto existing preparations were photographed with a digital camera mounted onto the dissecting microscope. Special care was given to the vertical adjustment of the ventral midline for a better comparison of patterns. Lighting aspects, however, were of no particular importance while analyzing the pictures by hand and in small numbers. This resulted in scattered illumination on the original pictures. To avoid the editing of original pictures prior to automated analysis, a new protocol has to be developed. Pictures need to be recorded in optimal lighting conditions with combined reflected and transmitted illumination. This is possible when the cuticula is pinned to a clear base, like two-component Sylgard® silicone, using very fine tungsten pins. Optimal orientation must be carried out even more accurately. This can be achieved by the use of alignment instruments like cross hair eyepieces. Caution is recommended when increasing the staining time to reach more high-contrast pictures, as surrounding areas or the whole cuticula might be discolored. An additional feature allowing the upload of manually charted MAS patterns would further increase the usability of the database. Standardized patterns might considerably improve genus determination, as the influence of small structures would be reduced. Furthermore, MAS patterns in specimens with discolorations or other disturbances could be included. In particular this concerns pictures which were classified as C images due to disadvantageous features found in the larva, not because of image capturing problems. Such disturbances might interfere with the computerized analysis. A number of more traditional morphological species indicators are located on the cuticula of larval Diptera, such as spinal bands and spiracles. While preparing the cuticula according to protocol, such indicators could automatically be included. If—due to close relatedness or other factors—the results of MAS analysis are not unambiguous, those other morphological characteristics might be used to substantiate species or genus identification. The MAS method thus complements traditional methods, rather than rivalling them. MAS methods, of course, are not restricted to forensically important Diptera. Many of the over 200 known dipteran families might not be forensically important, but could be included in a MAS database for species determination in other fields of entomology. Furthermore, if the data base can be expanded, identification might not remain the only objective. Not all dipteran larvae live in the same habitat and have the same behavior. Muscle equipment of larvae might reflect such differences. An indication for further analysis of muscle equipment and behavior could be given by muscle attachment site pattern comparisons in different species. Obligatory parasitic larvae for instance might have a reduced number of muscles due to diminished motility. Confirmation of this statement, however, needs further studies of larvae representing different breeding behaviors. Such a general view could lead to new insight in ecological systems. 7. Conclusions With the growth of the knowledge base and a complete description of all segments, there might not only arise more differences between species, but also some so far unknown similarities. In an ambitious project, a complete map could be drawn of all clusters in all accessible developmental stages of larvae. The tempting prospect would be that even a fragment of a larva would be sufficient to determine its species and developmental state. An increase in the precision of the software will therefore be essential, which in turn could lead to the ability to distinguish seemingly indistinguishable species (e.g., Piophilinae species). Finally, with a broad knowledge base and the corresponding software, species determination in dipteran larvae would become a quick and performable task for every interested individual able to take a digital picture of the larval cuticula. No entomological training and/or piles of field guides and keys with endless details would be needed!
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Author Contributions: Senta Niederegger developed the MAS method; Klaus-Peter Döge developed the methodology of image processing and the test plan; Marcus Peter and Tobias Eickhölter performed the experiments and the programming; Gita Mall provided research materials and facilities; Senta Niederegger and Klaus-Peter Döge wrote the paper. Conflicts of Interest: The authors declare no conflict of interest.
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