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MIMS: A Prototype for medical image retrieval Richard CHBEIR, Youssef AMGHAR, Andre FLORY LISI - INSA, 20 Avenue A. Einstein F-69621 Villeurbanne - France Phone : (+33) 4 72438595 - Fax : (+33) 4 72438713 e-mail : {rchbeir, amghar, flory}@lisi.insa-lyon.fr
Abstract With the advent of multimedia information, several problems have bounded traditional database approaches designed strictly for alphanumeric data. One of the many induced problems is the retrieving process. All current proposed systems approach the retrieval problem using one aspect of the concerned media (image, voice and video). Our prototype, called Medical Image Management System (MIMS), is based on a synergy of several approaches and conceived to yield an effective system appropriated to retrieve images in medical domain. The main goals of our system are to acquire significant information associated to medical imaging and answer complex medical queries. This paper presents MIMS prototype and show how the end-user is able to process easily both complex medical image retrieval and storage.
Keywords: Multimedia database, Retrieval, Iconic Interfaces, Prototype, Medical Image 1
Introduction
Information systems are witnessing nowadays a spurred use and great rise of multimedia information in multiple domains. Many systems manipulating both textual and multimedia data (image, voice, and video) have benefited of new utilities and possibilities. New multimedia data, in peculiar the image, are so rich in information and necessitate elaborated description mechanisms. The current technology has permitted to resolve certain induced problems. For instance, the problem of storage has found an adequate solution with the new digital technologies such as optical devices, the Compact Disk, worm, etc. It is possible today to store over one million images on one CD. Also the Displaying problem has been resolved with high quality visualization technologies. Nevertheless, the problematic of image describing and modeling still presents a great interest for researcher community. In fact, searching for x-rays, for instance, is not an easy task, and might be very complicated. We can search for either patient x-rays, specific x-rays, specific anomalies or even specific x-rays with specific anomalies. Current proposed multimedia systems in certain medical domains are built on retrieval mechanism using either factual image information (such as identifier, name or image date acquisition). Other approaches use the graphical aspect found in image content (as colors, textures, brightness and bounderies) and try to respond physicians queries. The disadvantages presented by each approach are particularly dangerous whenever image content is very precious for the users. For physicians, in particularly, a brain tumor visible in the right lobe of the patient x-ray is much more significant than a set of colors for instance. Hence, retrieval process must provide the possibility for physicians to retrieve medical images using an appropriate approach able to combine semantic and graphic (graphical properties and relations) aspects. To heal the mentioned lacks, a project has been started in our laboratory whose main objective is to elaborate efficient Medical Image Management System (MIMS). This paper snapshots the developed prototype allowing end-users to index and retrieve patients X-rays. On the one hand, our proposed approach gives the user all criteria's needed to build and search the
medical images databases [Chb 99]. Over a model built on context and content aspect, MIMS offers several possibilities to describe image within multifaceted image content (context, graphical and semantic). Several relations have also been defined to outline the structure (topological, spatial and semantical) of image objects. On the other hand, the reasons why we are focusing on providing and elaborating appropriate tools for easily managing medical images databases are: • Practical: because physicians or radiologists rarely have the time to search for an x-ray, especially when they manage several departments in the hospital, • Theoretical: for medical students and teaching institutions. • Technical: because most of proposed systems do not take into account the final user and are not adequate for non computer-science users. For these reasons, we propose an appropriate and efficient solution. Our implementation consists of using Internet1 or Intranet platforms, and allows physicians and radiologists to easily manipulate the medical databases, from their workstations, and retrieve images using any browser (Netscape, Internet Explorer, etc.). The rest of this article is organized as follows. A survey of various related works in this domain is given in Section 2. In section 3, the architecture of our prototype is discussed. MIMS graphical iconic interfaces are detailed in section 4. Conclusions and future directions are then presented in Section 5.
2
Related Works
This paragraph describes the general problem and shows why current systems are unable to satisfy the needs of medical domain searches. First of all, let’s imagine that when we search for an image, the first asked question is “how to describe it?”. For the same image, several relevant descriptions could be found. In essence, image description presents a basic problem for the retrieval process. It is so person-dependent and so related to several parameters such as context, image objects, application domain, etc. This partially reflects the difficulties encountered in describing (indexing) and retrieving process. [Adiba 90] and [Berrut 91] classify image description as follows: • Physical description: corresponds to numerical data constituted by image digitalization. • Logical description: reflects the image content. Here, external and internal characteristics might exist. The former ones deal with image context as patient name, sex, etc. The latter ones interact with image content. Here also, we can find graphical content description used to exploit forms, colors, textures and brightness, and semantic content description used to qualify and identify symbolic objects found in image. The next subsections describe how nowadays systems have approached the problem. 2.1 Contextual approach The context describes the external environment of image and considers it as a black box. An example of the medical image context could be the patient name, image number, incidence, image type, etc. The first proposed solutions have adopted this approach such as OSIRIS [Lig 1
MIMS prototype can be consulted on http://www.mims.cemep.asso.fr
94], member of important European project DIOGENE. OSIRIS necessitates starting the retrieval process by selecting the patient in PACS database ([Mas 96]), and then by choosing the appropriate image(s) listed in his file. In fact, as it's known, medical imaging is so rich in terms of content, and ignoring such aspects limits the physician’s search capabilities. Furthermore, to improve teaching and methods in topics such as Medical Engineering, Life Science, Medical Physics, etc. the contextual approach is so restrictive and limited but, on the other side, it's necessary and indeed useful. In other words, the contextual approach cannot alone satisfy the medical queries as the one listed in the example above. For that reason, almost all systems have integrated the contextual approach in order to be as adaptable as possible. 2.2 Graphical approach This approach considers the image as a white box and attempts to describe its graphical properties such as colors, textures, forms, brightness, etc. Pattern recognition and segmentation are generally used to calculate these properties. Recently, contextual approach has known a significant rise for several reasons: • Graphical properties are automatically calculated, • Graphical interrogation consists of comparing two images vectors, • Sorting of result by ordering images according to their relevance. Systems adopting this approach are numerous such as [Jef 93], [Ash 95], [Cio 98], [Gel 98], [Che 98], [Hue 98] and [Sch 99]. In spite of the great current interests of the graphical approach, it is still too open-ended to be appropriate in several domains for the following reasons mentioned underneath: 1. Searching for shape, like circle for example, in museum database would give a heterogeneous result such as a very known person portrait or a loon picture, 2. The used procedures are time-consuming, 3. It is still not adaptable in several domains such as medical ones where the miscellaneous digitalization techniques (Scanner, IRM, X-ray, etc.) are used and the variety of human body bound the graphical approach utility. 4. When dealing with domains where image components are as important and significant as the image itself (such medical domains), it is recommended to use an approach independent of image support, incidence, zoom, etc. in order to warrant the system as coherent and efficient as possible. 2.3 Semantic approach In this approach, we are mostly concerned with image components (or objects) as defined semantically in real world. Since subjectivity and imprecision are usually associated with specifying and interpreting semantic content, sense description associated to image constitutes sometimes a painful task; Kennedy’s photo when he was young could be described as the image of a child, or as the image of the future president of the USA. Image description depends upon multiple ingredients: application domain, person culture, expert level, etc. For example, a query on “heart” may retrieve all heart X-rays in medical domain and a photo of two lovers in pictorial one. Because manual procedures are used (key-words with QBIC [3]), or legend (text) indexed classically ([Mec 95], [Leu 95], [Sut 97], [Cha 97], [Yan 97], [Li 98] and [Jia 99]), the golden age of this approach was brief.
The appropriate approach in medical domains must take into account firstly the complexity observed when describing semantic content of images (objects, their relations and properties) and secondly the graphical aspect of certain objects. In cytology domains, for instance, the semantic description is only limited to cells name, but the real significance is instead replaced by the graphical location of each cell. Hence, such approach must be as general as possible to be able to combine flexibility and efficiency between different medical domains.
3
MIMS Architecture
3.1 The description of Medical Image Facets Medical imaging is peculiar and requires various parameters. “Retrieving all x-rays for patients, older than 50 years, having dangerous tumor on left lung above and approximately touching another tumor” presents a typical example of physicians, researchers and students queries. This example obviously shows that one medical image facet is so restrictive and unable to satisfy end-users needs. In fact, searching criteria in medical information systems rely not only on image context (patient older than 50 years), but also on semantic (dangerous tumor) and graphical (on left lung, above and touching) content. Therefore, we should not ignore the incertitude aspect (approximately) found in medicine as well. This means that efficient retrieval approach must take into account all these aspects. For that reason, we believe that medical retreival system cannot exist without efficient describing and indexing process. As we already mentioned, medical image possesses multiple facets and the lack of current approaches resides in image describing. In fact, they concern all interest in the retrieval process [Tam 99]. 3.2 MIMS features In order to provide an appropriate approach and system, we have implemented a Medical Image Management System (MIMS) whose main features concern the following aspects: 1. Storage of medical images: to support various complex data types, including text, numerical and temporal data, images and voice reports. 2. Portability and independence concerns: since concerned users need to be connected from hospital, clinical or university, the system must be portable and independent from operating system. 3. Simplicity of use and design: the browser must be simple enough for non computerscientist users. 4. Flexible query methods: flexibility to handle complex queries combining several image attributes. To process query such as "Find an image of brain where tumor is located in the right lob", the retrieval system must be able to search on the basis of multiple criterias such as content (tumor) and spatial location (right lob). To achieve all these tasks, MIMS is composed, as shown in Figure 1, of several modules detailed below where each is designed to support one or more functionalities.
3.3 MIMS Modules 3.3.1
The inserting and indexing module
It gives the possibility of images description and insertion into the database. During the insertion process, image attributes are created. MIMS approaches the problem of indexing within a semi-automatic solution: via an iconic graphical interface, the user is asked, as explained in the next sections, to describe the image content (in terms of anomalies and objects), to alter or override the results when necessary (in terms of spatial and semantic relations). Afterwards, the system translates image content into SQL language and sends it to the JDBC driver. The last one, with the collaboration of JDBC/ODBC bridge, stores data in the appropriate databases (images are sent to image databases, general data are sent to EMD database, etc.). The inserting and indexing process is detailed in the prototype section. 3.3.2
MIMS databases
The MIMS database schema is based on entities that describe the data related to the medical domain. The main element that relates all heterogeneous databases is the Electronic Medical Dossiers (EMD) gathered on: 1. General data about the patient: age, sex, birthday, etc. 2. Medical data: • His medical identity: antecedents, blood pressure, weight, etc. • Examination reports (text or audio) • Medical images: X-rays, Scanner, IRM, etc.
Indexing and Storage
Client / Internet
MIMS Serveur
Update
Thesaurus Image Database Retrieval and interrogation
Query Voice Database
API JDBC JDBC driver manager JDBC / ODBC Switch
Figure 1: MIMS modules
EMD Database
Images and Voice databases (1000 X-rays and 800 voice reports) contain only physical data whereas EMD database (10000 records approximately) constitutes the link between administrative data and other databases. EMD database contains, for instance, all images descriptions extracted during the insertion and indexing process. For the user, there is no evidence that the different data items are handled by different applications running different databases on different servers. 3.3.3
The thesaurus
The thesaurus maintains all domains–specific information used at every step of processing in MIMS. It represents a dictionary in which main components are interrelated. It is used to normalize indexing process and make indexing and retrieval processes more coherent, easier and efficient. The thesaurus is conceived around three medical entities: • Anatomic Organ (AO): presents the medical organ occupied by the image such as brain, hand, lungs, etc. • Medical Region (MR): describes the internal structure of the AO. It allows to powerfully locate each anomaly. For example, the left ventricle, the right lobe, etc. A set of polygons determines the MR form. • Medial Sign (MS): is a medical anomaly (such as tumor, fracture, etc.) identified and detected by physicians, On the one hand, medical entities possess different types of conceptual relations. These relations can exist between either medical signs (MS/MS) or, Medical Region and Medical Sign (MR/MS): • The synonymy relation: relates a secondary2 medical entity to a primary one. The medical vocabulary is so complex that some equivalent concepts may be different from domain to another. For instance, a tumor is a synonym to a cancer. • The hierarchy relation: defines partial order relation between medical entities in order to classify medical terms and extend queries. It groups generalization / specialization relation (anomaly and tumor) and/or composition relation (head and brain). • The compatibility relation: connects Medical Region to Medical Sign. This relation is used essentially to sort MS according to compatible MR. For instance, at the left ventricle, only tumor could be found. • The multi-linguistic relation: to assure an international compatibility between entities. On the other hand, the medical entity, designed in MIMS, is associated to a graphical or geographical shape. It’s so important, when dealing with non-computer specialists, to use convivial interfaces. The concept of shape is firstly used to associate a help image to the Anatomic Organ and an icon to Medical Sign. Secondly, it associates a set of polygons to the Medical Region geometrical form. These graphical representations allow MIMS to build its graphical iconic interfaces proposed and detailed later in this paper. Also, a set of graphical characteristics is attached to each shape in terms of texture, brightness and colors. MIMS thesaurus interfaces will not be detailed in this paper.
2
A term semantically near to another one.
3.3.4 The retrieval module It is used interactively by the user to retrieve medical images from the databases. Queries might be completely generated by the user, or regenerated by the system itself regarding the previous queries and the user choices. The comparison between images is supported by a similarity function, which, via the tree form of the thesaurus, determines the relevance and the order in which medical images are displayed. In function of the user certitude called “Matching Level” which is a value between ]0, 1], the function determines the space in which the query is applied. For example, when the user describes the anomaly as "tumor", the retrieval module generates, in function of Matching Level and via the thesaurus, a set of queries like retrieving all images containing "tumor" or " malign tumor " or "cancer" or "sarcoma".
4
MIMS Prototype
The section exposes several interfaces used in MIMS to provide an efficient retrieval system. The reader is invited to consult our conceptual model [Chb 99] for more details. MIMS is implemented in Java programming language in order to develop a platform-independent database–independent application. It may be described as relatively simple, object-oriented, distributed and portable system. Java programming language permits to develop either: 1. an applet: a program that is downloaded over the Internet and runs on the client, 2. an application: a program that resides on the client side, MIMS modules are built using Applets. The Java Development Kit (JDK Version 1.1) released in the Spring of 1997, provides a Java interpreter and compiler, but the most important is the Java DataBase Connection module called JDBC. The former one permits to connect and remote heterogeneous databases using an API supplied in JDK. The JDBC Driver Manager is used to achieve different tasks of storage and retrieval of heterogeneous databases (Access, Oracle). In this section, we give details of specific image interfaces of MIMS.
JAVA Applets JDBC/RMI ou TCP/IP
JDBC API JDBC Driver Manager JDBC/ODBC Switch ODBC Driver Manager
Direct JDBC Driver
ODBC Driver ODBC
BD
BD
Figure 2: Server - DBMS Connection
4.1 User Access To be connected to MIMS, the user has to be identified by a login and a password. Depending on the workstation and user access privileges, a certain number of applications are usually available. For instance, only the specialist administrator(s) has the possibility to index and store images into databases. Thus, it’s for the administrator to define the privileges and the properties of each user. Students, for example, do not have the right to see confidential data of patients (such as name, address, etc.) but have only the ability to personalize their environment and query the databases. 4.2 Administrator modules As mentioned above, for confidentiality reasons we separate between server tasks (like storing images, updating the thesaurus and defining users privileges) and client tasks. To achieve server tasks, the administrator has access to several interfaces such as Image Context Indexing, Image Content Indexing, Thesaurus update and Users Maintenance Interfaces. 4.2.1
Image Indexing modules
To store an image, the administrator has to apply context and content properties. The former properties concerning image such as Patient, Image Path, Acquisition date, Medical domain, incidence, etc. are managed as traditional records in Image content indexing module (Figure 3). The administrator can also choose information from a previous list for the same patient.
Figure 3: Image Context Indexing Interface Using the image content indexing interface, the administrator can easily outline the image content. To describe its content, the administrator chooses between using either the analyzed image itself or the pattern corresponding to the Anatomic Organ (AO) occupied by the image
(brain, lugs, hands, etc.) (Step 1 of Figure 7). Actually, even if he chooses to describe the image using the AO, he can, at any time, display the image in different screen. He can also change the incidence and level of the chosen AO in a manner that it corresponds to the analyzed image to store. The use of the pattern permits to have a unified view, and then a coherent manner to store spatial image attributes independent of graphical operations (scaling, rotation, zooming, etc.). After the administrator’s choice, the analyzed image (or the AO pattern) appears with or without its Medical Regions (MR) depending on his choice (Step 2 of Figure 7). We denote that these MRs determine the internal structure, predefined in the thesaurus, of the AO. Clicking over a MR launches a procedure that determines, via the thesaurus, compatible anomalies to display (Figure 4). With the semantic display of each icon when the mouse passes over, the administrator can easily locate the desired anomaly or type the anomaly name (or code). If the thesaurus does not contain such code, the administrator is notified. The navigation through the thesaurus, when clicking on a MR, for both administrator and normal user could be assured by: 1. button + 2. button -
means more detailed and/or specific information depending on anomaly choice, means more general or back.
Figure 4: Thesaurus Access Figure 4 shows the starting part of the thesaurus access. The button A, for instance, presents the global information of all anomalies starting by A letter. The user can have more specific anomalies by clicking on è button. The thesaurus access (either Itemized or Iconified) is integrated into the indexing and the interrogation interfaces. The anomaly positioning launches "MR graphical procedure" that finds and creates the appropriate spatial relations between anomalies and MR (Step 3 of Figure 7). In MIMS, two types of spatial relations between MR and Anomaly are automatically calculated and taken into account: topological and directional. Topological relations are computed via the polygonal forms associated to each object, while directional ones are computed via their barycenters. On the one hand, MIMS considers only topological relations MR/anomaly such as Contain, Overflow, Externally and Internally touch shown in Figure 5. MR1
X
MR1 Contains X
MR1
X X Overflow MR1
MR1
X X Externally Touchs MR1
MR1
X X Internally Touchs MR1
Figure 5: Topological Relations between Medical Region and Anomalies
Several directional relations are then possible: North, East, West, South, Left, Right, lowright, high, left-high, etc. Nevertheless, only four directional relations have been taken into account: “North, East, West, South”. Concerning “High, Low, Right and Left” relations, they are equivalent to those taken into account because Anatomic organ pattern determines a referential angle. Thus, the possibility that each MS may have two directional relations with its MR is always maintained. So the Southeast relation, for instance, is indirectly taken into consideration. Moreover, another procedure, called “anomalies graphical procedure”, is launched to searches for other anomalies (in the same region) and calculates graphical relations between them. Also, directional and topological relations are considered. Figure 6 shows the set of topological relations computed in MIMS. Concerning directional relations between anomalies, only two relations are taken into consideration: High and Left. In fact, equivalence between directional relations permits to neglect the implicit ones. In other words, if X is higher than Y, then Y is lower than X and vice versa. The result of all those relations, MR and anomalies, is shown directly beside the analyzed image (Figure 7 at Step 3).
Disjoint
Mix
Cover
Externally Touch
Internally Touch
Contain
Figure 6: Topological relation between anomalies Administrator intervention is omnipresent during all the indexing phase. He can add any semantic relation and change any spatial relation already automatically calculated. Finally, the image storage into the database depends on administrator validation. We denote that JDBC driver allowing the indexing process to be transparent assures the database connection. 4.2.2
Users Maintenance module
It allows the administrator to define users' privileges and environment parameters. Privileges are defined as Total control for administrators, Total Consultation for physicians and radiologists, and Partial Consultation for students. Total control allows user to manage all the system (storing, indexing, retrieving), Total consultation permits only to retrieve images and textual data without any restriction, and Partial consultation gives the ability to retrieve data without confidential information such Patient name, Social Security Number, etc. Environment parameter determines where to save user choices such as icons and password. It includes also the name and IP address of workstation. These parameters are used to store cookies on workstation that MIMS can use to accelerate and secure access. The administrator can also view all defined users in a grid.
Step 1 Step 3
Step 2
Figure 7: image content Indexing Interface 4.2.3
Module of Medical Designer
This module provides a tool for end-user to manipulate images (physical aspect). It is mainly used by the administrator to design, name, and integrate medical regions into the thesaurus. It can be also used by the end-user for other operations. Three categories of operations are used: § General image operations: images filtering, image resizing, Copy/Cut/Paste, zooming, rotating, scaling, brightening, contrasting, edges detecting, bounds detecting, flips, adding shadows, morphology operations, etc. § Analyzing operations: measuring, histogram, Fractals, Lanes, etc. § Designing medical regions: using this module, the administrator can design via the appropriate tools (toolbar and menus) the form (rectangle, circle and polygon) and give a name for each medical region (Figure 8).
4.3User modules In this section, we will address MIMS from the user’s perspective and show how the user can retrieve images as simply as possible.
Figure 8: Region Designer interface 4.3.1
Interrogation and retrieval module
The MIMS interrogation user-friendly module demonstrates the ability to combine efficiency and conviviality. Figure 9 shows a screen shot of the main retrieval interface. Therefore, This module uses all components of MIMS to retrieve relevant images. We mention that the classical interrogation consisting of retrieving images using some alphanumeric attributes such Patient Number is used but will not be exposed here. In fact, querying with MIMS interrogation module is an easy task. It uses the same vision process used in insertion module. The user has only to choose the medical organ with an appropriate incidence and stage, to put the anomaly(ies) in MR. This operation launches a process (thread) that calculates image objects and their relations. Afterwards, the user can precise the certitude level via several useful parameters for similarity. Matching Level determines the search level between semantic relations of the thesaurus (synonymy and hierarchy). Anomaly similarity level consists of choosing one of these options: “Same Anomalies; Same Anoamlies and maybe Others; Any of anomalies but no others; Any of anomalies and maybe others”. Spatial similarity level fixes the location certitude in terms of: 1. 2. 3. 4. 5. 6.
Same positionning; Same direction and same distance; Same direction and other distance; Same distance, same topological relations but any spatial ones; Same distance, any topological relations and same spatial ones; No spatial constraints
Thus, the user can customize the displayed result. He can choose to display, for instance, the result in different screens (Cascade), with an abstract on each image. MIMS transforms the visual query describing the user needs into SQL statements and sends it JDBC listener. The last one queries the databases and sends results to the user. The relevant images are displayed according to the user choice.
Figure 9: Graphical Interrogation Interface Figure 10 shows the result of the query shown above where images are ordered via their relevance, viewed in a tile manner and with an abstract of their content. If the result does not correspond to the user need, he can demand a new retrieval after choosing the most “Pertinent” images in his sense. In this case, the query is modified automatically by the system and new results are displayed again. This task is achieved within the user choice and the thesaurus. The role of thesaurus is to select the common objects (if exist) of the user relevant images with the most close neighborhood anomaly found in all others.
5
Conclusion and future work
The main motivation for this work has been to provide a flexible system for answering semantic queries in medical multimedia applications using content-based images. The lack of current systems resides in the fact that they approach the problem from different views. They ignore image multifaceted aspects, and use only one facet, which is dependent on application domain. This paper describes the MIMS prototype and shows how MIMS approaches the problem of medical image description and presents general medical objects and relations. The MIMS architecture is designed to answer several functionalities and guarantee such an independence aspect between modules in order to use our approach in several medical domains, reuse in other domains and with other media (voice, video, etc.). Thus, we have presented our interactive graphical WEB iconic interfaces allowing complex and efficient content-based image storage and retrieval.
Figure 10: An example of Retrieval Result for trusted users Our current interest is to involve another important aspect into our approach: the image evolution content. For physicians, evolutionary content-based queries are as important as semantic ones. It allows physician to carry out his diagnostic on presenting chronological images sequences. To study maturity development, disease progression, medical therapeutic, traumatic event, etc., the image content evolution must be integrated into the database. Therefore, we aim to integrate an online system into MIMS prototype to guide physician during storage and retrieval processes. Another perspective is also previewed which is the autoconstruction of the thesaurus. Our future proposition in this optic would be conceived on a certain number of rules that would be applied during the indexing phase and try to construct a hierarchical thesaurus. Finally, as we truly believe that our approach is portable and adaptable in several domains (including non-medical ones), our future work intends to integrate pictorial databases.
6
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