Quality Assessment of Compressed MR Medical Images using

Int. J. Pure Appl. Sci. Technol., 7(2) (2011), pp. 158-169

International Journal of Pure and Applied Sciences and Technology ISSN 2229 - 6107 Available online at www.ijopaasat.in Research Paper

Quality Assessment of Compressed MR Medical Images using General Regression Neural Network Bheshaj Kumar1, G. R. sinha2 and Kavita Thakur3,* 1,3 2

School of Studies in Electronics, Pt. Ravishankar Shukla University, Raipur, (C.G.), India. Shankaracharya Group of Institutions, Junwani, Bhilai, (C.G.), India.

* Corresponding Author, E-mail: ([email protected]) (Received: 12-10-11; Accepted: 20-12-11)

Abstract: Medical imaging plays a major role in contemporary health care, both as a tool in primary diagnosis and as a guide for surgical and therapeutic procedures. Compression of radiological images is an effective mechanism for storage and transmission. The use of such images for teleradiology is of increasing importance, with one of the main reasons being the ability to call upon remotely located diagnostic experts. Whilst many researchers have addressed the problem of how the degradation of image quality with compression ratio affects observer-based diagnostic accuracy. In this work, we propose a quantitative analysis of quality for lossy compressed magnetic resonance (MR) images, and their influence in automatic tissue classification. Peak Signal to Noise Ratio (PSNR), as quality measurement, is not enough if we have medical images. So we need to find out new quality measurements not based in perception to make a quantitative analysis for medical image compression. This paper proposed a scheme which combines the Artificial Neural Network and technique of Structure Similarity Index Measurement (SSIM) to improve the issues. We feel that the proposed framework is in the right direction towards the achievement of this goal. Keywords: Image Compression, MR Images, Artificial Neural Network, Structure Similarity.

1. Introduction In view of the increasingly important role played by digital medical imaging in modern health care and the consequent blow up in the amount of image data that have to be economically stored and/or transmitted, the need for the development of image compression systems that combine high compression performance and preservation of critical information is ever growing. Image compression algorithms take into account the psycho-visual features both in space and frequency domain and exploit the spatial correlation along with the statistical redundancy. However, usages of the algorithms are dependent mostly on the information

Int. J. Pure Appl. Sci. Technol., 7(2) (2011), 158-169.

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contained in images [1]. A practical compression algorithm for image data should preserve most of the characteristics of the data while working in a lossy manner and maximize the gain and be of lesser algorithmic complexity. In medical images, lossless compression, when an image can be perfectly reconstructed from a file compressed, can be used without controversy. However, in lossless compression, advantages are limited, only files reduced in a 20 % of its original size can be obtained. For larger compression ratios, the original image can not be totally recovered; it is only possible to obtain an approximation. In this work we present a quantitative analysis of quality for lossy compressed Magnetic Resonance (MR) images, and their influence in the automatic tissue classification accomplished on these images [2]. In general almost all the traditional approaches adopt a two-stage process, first, the data is transformed into some other domain and or represented by the indices of the codebook, followed by an encoding of the transformed coefficients or the codebook indices. The first stage is to minimize the spatial correlation or to make use of the correlation so as to reduce the data. Discrete cosine transform is used practically in almost all image compression techniques [3-9]. As the modern data mostly nonlinear and is either complex, in great magnitude or too less data is available. Artificial Neural Networks is a widely discussed and re-studied topic in recent years. It refers to an imitation of biological neural network information processing system [10-11]. To the biological point of view, the artificial neural network is a simple model of human brain. The simple operation element which corresponds to the brain's neurons and many connections throughout the network of neurons is known as the neural network. Now the two basic tasks that the neural networks can perform are regression and classification. In fact, the advantages of neural networks can learn non-linear system, excellent learning ability, good fault tolerance and the characteristics of highly parallel computing power. Neural networks are fast, reliable, easy to manipulate thus very effective for real time applications [12-19].

2. Background of Related Work Information content weighting leads to consistent improvement in the performance of image quality assessment algorithms. With information content weighting, even the widely criticized peak signal to noise ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. The best overall performance is achieved by combining information content weighting with multiscale structural similarity measures. This novel weighting method proposed by Z. Wang et al leads to significant and consistent performance improvement of both PSNR and SSIM based image quality assessment algorithms. The optimal weight for pooling should be directly proportional to local information content measured in units of bit [20]. Z. Wang et al proposed weighting method for the SSIM-based method as compared to the MSE/PSNR-based method to in-corporate a recent model of human visual speed perception and model visual perception in an information communication framework. Improved video quality assessment algorithms are obtained by incorporating the model as spatiotemporal weighting factors, where the weight increases with the information content and decreases with the perceptual uncertainty [21]. Many recently proposed perceptual image quality assessment algorithms are implemented in two stages. In the first stage, image quality is evaluated within local regions. This results in a quality/distortion map over the image space. In the second stage, a spatial pooling algorithm is employed that combines the quality/ distortion map into a single quality score. Minkowski pooling, local quality/distortion-weighted pooling and information content-weighted pooling.


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All three methods may improve the prediction performance of perceptual image quality measures, but the third method demonstrates the best potential to be a general and robust method that leads to consistent improvement over a wide range of image distortion types [22]. A perceptual image similarity measure can be used to estimate perceived image quality, by measuring the similarity between a distorted image and a reference image that is assumed to have perfect quality. Z. Wang and E. P. Simoncelli proposed a new image similarity measure that does not require a precise registration process in the front, and naturally combines a number of invariants into one simple measurement. A measurement of structural similarity (or distortion) should provide a good approximation of perceptual image quality. It has been shown that a very simple SSIM algorithm provides surprisingly good image quality prediction performance for a wide variety of image distortions [23]. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. Z. Wang, L. Lu and A. C. Bovik proposed a new philosophy in designing image and video quality metrics, which uses structural distortion as an estimate of perceived visual distortion [24]. A multi-scale structural similarity approach for image quality assessment, which provides more flexibility than single-scale approach in incorporating the variations of image resolution and viewing conditions. Experiments show that with an appropriate parameter setting, the multi-scale method outperforms the best single-scale SSIM model as well as state-of-the-art image quality metrics [25].

3. Methods 3.1 Lossy Compression Algorithm JPEG To make this work we used JPEG-LS( Joint Photographic Experts Group near lossless) as algorithm of compression with loss because its implementation is easy, it uses little memory space and it has low computational cost [5-6].The algorithm created by the Joint Photographic Experts Group (JPEG) was designed to compress gray scale images. The JPEG-LS scheme includes a near lossless mode in which the predictor of the pixel value may be constrained to be within a certain absolute error, instead of being exact as it is in lossless mode. This approach can achieve impressive performance without introducing the undesirable visual artifacts (such as blurring or blockiness) that are a feature of transform based lossy compression. JPEG originally was a lossy compression algorithm, although the 2000 version incorporated lossless compression [7]. Lossy compression means that when we decompress the image, we obtain an image different to the image before compressing. This implementation uses the Discrete Cosine Transform, which is calculated using integer numbers; this is the reason for what the algorithm is fast. One characteristic that makes JPEG very flexible is its ability to adjust the compression rate. If we specify a very high compression, we will lose quality significantly, and we will obtain small files. If we specify a low compression, we will achieve a quality similar to the original file, and we will obtain larger files. This loss of quality is accumulative. This means that if we compress an image and then we decompress it, we will obtain a specific image quality; but, if we compress it and decompress it again, the loss will be bigger. Every time we compress and decompress the image, this will lose some quality. The JPEG compression algorithm is based on two human’s visual defects: the first one is that eyes are more sensitive to the luminance’s changes than chrominance’s changes; this means that human beings perceive better changes on brightness than changes on color. The other one is that eyes perceive easier little brightness’s changes over homogeneous zones than over zones with important variations, for example at the edge of objects. We used multi-stages


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compression for this work [8-10]. This consists in making a lossy compress of the image, then adding the resulting image and the error image, this last one obtained from the subtraction between the original image and the compressed image (See Figure 1).The consequence is not a strictly lossless compression, but a high compression rate with few loses is obtained, especially when using a good compression technique as the algorithm JPEG-LS.

+ Original Image

JPEG-LS Compression Algorithm

+

+

Multi-Stage Compressed Image

Error Image

Compressed Image

Figure 1: Block diagram of multi-stage compression system.

3.2 Classification Algorithm GRNN In order to segment images, through classification of different tissues, and to evaluate in which way classification is affected by compression we used an algorithm based in Generalized Regression Neural Networks (GRNN). They perform well on noisy data then say Back-propagation Neural Networks (BPNN), if the available data is large enough. That is one of the reasons the GRNN are being used in medical classification, predictive and diagnostics problems because a lot of noisy data is presented in such cases. It is an accurate and robust method, being t obtained the results practically independent of the expert [11-13]. GRNN are a kind of normalized Radial Base Functions (RBF) networks, where a cell of the hidden layer correspondent to each pattern of training exists. The neural-networks algorithm used for processing biomedical and/or biological signals is based on the Bayes-Parzen classifier and its architecture is based on the architecture of probabilistic networks or Radial Base Functions [15]. This kind of networks is based on the Non-Linear Regression Theory. They achieve a good approximation or mapping from input-output functions using training data [16]. In this case the approximation is used to obtain at the network’s output different values to each kind of tissue to recognize or classify. The general structure of a GRNN is shown in figure 2. The GRNN infrastructure consists of four layers input, hidden, summation and output layer. The first hidden layer contains the pattern units. The pattern units contain the important functional. Each pattern unit represents information on one training sample. Each pattern unit calculates the probability on how well the input vector fits into the pattern unit. In the second hidden layer there is only one summation unit. Here it is decided upon the individual results of each pattern unit in which pattern the input vector finally belongs. The output unit performs again a calculation to give the output a physical meaning [17-18].


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Figure 2: Block diagram of a typical GRNN.

4. Image Quality Assessment Image Quality Assessment in Image Processing plays an important role, as image processing algorithms and systems design benchmarks to help assess the best or the quality of the results. At present more commonly used by the image quality index for the assessment are the Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR), respectively, are defined as follows:

(1) (2) where N is the size of image, xi and yi are the gray level of pixel of original image and test image. However, these common approach, focused on the image gray value of the mathematical model to quantify the numerical standards, although with an objective assessment, but not all of the assessment results can meet the human visual judgement. By Figure 3 can be found in the Test Signal 1, Test Signal 2 and Original Signal, Error Signal of the MSE results are the same, but the human visual judgement can only discover that the Test Signal 1 is closer to the Original Signal [20].

Figure 3: MSE distortion of the signal difference calculation.


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4.1 Structural Similarity Index (SSIM) Universal Quality Index and Structural Similarity index applied to still image evaluation criteria, the results showed that the two kinds of gray scale images were superior to focus on the mathematical degree of statistical indicators. Structural Similarity Index (SSIM) taking into account the image of the brightness, contrast and structural and comprehensive representation of the overall image quality. The typical diagram of the structural similarity (SSIM) measurement system is shown in Figure 4. Signal x Luminance Measurement

Contrast Measurement

Luminance Comparison

Contrast Comparison

Signal y Luminance Measurement

Contrast Measurement

Combination

Similarity Measure

Structure Comparison

Figure 4: Diagram of the structural similarity (SSIM) measurement system. The SSIM index is a full reference metric, in other words, the measuring of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is designed to improve the traditional methods like PSNR and MSE, which have proved to be inconsistent with human visual system. SSIM is also commonly used as a method of testing the quality of various lossy image/video compression methods. Using SSIM index, image and video can be effectively compared.SSIM comprehensively indicates the structural similarity of the overall quality of the images which include the luminance, contrast and structure of images [22-25]. The SSIM is defined as: (3) where (4) (5) (6) and ,


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,

The SSIM index is a decimal value between 0 and 1. A value of 0 would mean zero correlation with the original image, and 1 means the exact same image. To perform the adaptive image assessment, we combined neural network and SSIM to establish the new single-layer percetpron. By the definition of SSIM, (7) is used and extend as (8). ] (7)

(8)

where w1 = α , w2 = β , w3 = γ , x1 = log(l(x, y)) , x2 = log(c(x, y)) , x3 = log(s(x, y)) As shown in Figure. 5, is the single-layer model according to the established formula.

log[l(x,y)]

α

log[c(x,y)]

β γ

∑

Q

log[s(x,y)] Figure 5: Single layer model for SSIM measurement.

5. Databases and Implementation We get simulated brain RM images from MNI's BrainWeb database [26]. Image database consisted on T1, T2 and PD weighted images (mutlispectral RM images).We processed 1500 images for our work.


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5.1 Training algorithm of GRNN for classification

Converted into Vector 1xN

Converted to Grayscale Images

Database Images

Divide Dataset into Training and Testings sets

Training GRNN with Training sets

Figure 6: Training algorithm of GRNN for classification From the image database, vectors of the images are captured. First of all, each image is converted into grayscale image. The mean of each image is computed by taking of each image in column-wise manner. Thus all the images in the database are converted into vectors of dimension 1xN. In this way, we get hold of 1500 image vectors. Each vector corresponds to an image. Out of these images, about 60% (900 images) are applied for training of network and remaining 20% (300 images) are used for validation testing of network and remaining 20% (300 images) are used for testing of network performance. Training Dataset Total instances = 900 Validation Dataset Total instances =300 Testing Dataset Total instances =300

5.2 Testing algorithm of GRNN

Test Images

GRNN

Final Result

Classification

Comparison with

Original Image

Figure 7: Testing algorithm of GRNN The first stage consisted in compressing the images with different compression ratios. For these images the PSNR was computed as a quality index. During the second stage, the original image was used as input to the GRNN classification algorithm. Then, lossy compressed images were used as input in the same scheme of classification.


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In order to compare both tissues classifications, the features of SSIM, including the overall image brightness, contrast ratio and image structural comparison are utilized. Since the image processing technique of SSIM is closer to the human eye, SSIM and the neural network are combined to perform the adaptive image quality assessment (see Figure 5).

6. Simulation and Results The developed algorithm was implemented on MatLab® 2006b version, using standard functions Image Processing toolbox and Artificial Neural Network toolbox. The multi-stage JPEG compression is implemented on T1, T2 and PD images, with different compression levels, from Q=1 to Q=100. At a first stage, PSNR value using Equation 2 is computed on all the images, as an estimation of its quality. A Figure 8 shows the variation of PSNR value of images with compression level. We can see how at high Q, a value above 85, image quality (PSNR) increases quickly. For middle levels of compression, PSNR values stay between 35 dB and 45 dB. At last, for lower Q values, under 20, quality decreases again. Figure 9 show relation between file size vs. compression level. There is not big percentage reduction of file size with the value of Q. This is because RM images have high texture content. We could define a Q value below which we cannot observe big improvements in reduction of file size in function of quality losses. The supporting values of PSNR for image T1, PD and T2 with respect to compression level(Q) is given by Table 1. Similarly Figure 9 and 10 have been plotted.

Table 1: Compression Level (Q) and PSNR (dB) for T1,PD and T2 images. Q

T1

0

30.67

5

PD

T2

Q

T1

PD

T2

30.16

29.90

55

40.68

37.97

35.79

30.85

30.51

30.21

60

41.19

38.13

36.12

10

34.75

33.27

32.63

65

41.72

38.07

36.23

15

35.83

34.51

32.94

70

42.12

38.96

36.84

20

36.98

35.03

33.47

75

43.15

40.03

38.46

25

37.62

36.34

34.05

80

44.09

41.43

39.98

30

38.17

36.58

34.29

85

45.11

42.89

41.75

35

38.89

36.87

34.71

90

47.23

44.32

43.94

40

39.47

37.07

35.01

95

50.17

49.76

48.67

45

39.92

37.69

35.16

100

62.39

62.38

62.35

50

40.01

37.73

35.26


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Figure 8: PSNR variation (as quality coefficient) vs. Compression level (Q).

Figure 9: File-size variation vs. Compression level (Q). At second stage, we used a classification scheme based on GRNN to find the answer, how could affect this quality reduction to the tissue classification? This algorithm was used obtaining very good results in previous work [14]. Tissues were classified using both original images and lossy compressed images as input data. Three tissue types were classified: Cerebrospinal Fluid (CSF), Grey Matter (GM) and White Matter (WM). In order to evaluate quality and accuracy of the obtained classification, The l(x,y),c(x,y),s(x,y) and SSIM values for, each image is calculated. Figure 10 shows average variations in SSIM for each classified tissue as a function of the compression level Q. Continuous lines show SSIM values for original images. We can see that for CSF, compression markedly deteriorate the classification quality, much more than for GM and WM. That is because CSF has least regions (few pixels). But in WM, the ones occupying larger regions, compression affects it less, as it was expected. The most interesting conclusion is that when compression ratio is low (high Qvalues), SSIM are larger than those obtained from original images. This is due to the fact that compression produces a filtering effect on images that helps to have input characteristics improving final classification.


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Figure 10: Average variation of SSIM vs. Compression level (Q).

7. Conclusion We accomplished a quantitative analysis of lossy compressed Magnetic Resonance (MR) images quality. Multi-spectral RM images T1, T2 and PD were compressed using JPEG multi-stage compression with different compression levels. In order to quantify quality, we used PSNR, being that perceptive measurements too dependent on the observer; therefore they are not useful to make a quantitative analysis. Therefore, the Structure Similarity and Artificial Neural Network for image quality assessment are investigated. The SSIM can retain structural characteristics of the image. By using the ANN properties to find the coefficient of SSIM, all kinds of multispectral MRI images, image quality assessment index can be established. It can achieve adaptability for the image quality of different types, become the optimization of image processing parameters, and obtain both image structure and image quality of multispectral images. Experimental results have demonstrated that the proposed approach can make the image quality of different types to achieve adaptability.

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