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Medical Image Compression and Feature Extraction using Vector Quantization, Self-Organizing Maps and Quadtree Decomposition Guy Cazuguel*, Andras Czihó*#, Basel Solaiman*, Christian Roux* * ENST Bretagne, Dépt. Image et Traitement de l'Information B.P.832, 29285 Brest Cedex, France Laboratoire de Traitement de l'Information Médicale (LATIM - EA 2218), # Technical University of Budapest, dept. IIT, Budapest, Muegyetem rkp. 9, 1521, Hungary

Abstract - Vector Quantization (VQ) is an efficient image compression approach. Among the different existing algorithms, Kohonen's Self Organizing Feature Map (SOFM) is one of the wellknown method for VQ. It allows efficient codebooks design with interesting topological properties to be performed. Furthermore, use of VQ for compression delivers basic information on the image content in the same process. However, in order to preserve the diagnostic accuracy in medical applications, the block size must be restricted to small values (e.g. 3x3, 4x4), which limits the compression rate. We propose to improve the compression performance by using several codebooks containing codewords of different sizes, according to the quadtree decomposition of the images. Results are compared to those provided by the standard JPEG image compression algorithm. Finally we introduce and discuss the signature maps of images using compression information.

I. INTRODUCTION Images are very important in medicine for diagnostic and therapy. Nowadays medical images are often used and stored in digitized form. This leads to very high storage and/or transmission time requirements, because of the high image size and the high number of images to be stored. Therefore, compression of medical image data is a crucial question, as well as image retrieval. There are two main families of image compression methods: lossless image compression techniques and lossy ones. Lossless algorithms guarantee a perfect reconstruction of every pixel, but they have the disadvantage of being limited in term of compression rate. Lossy techniques allow larger compression rates to be reached, while introducing some distorsion in reconstructed images. Therefore, they have to be used carefully, especially in medical field: compressed images have to lead to the same diagnosis than the original ones. To improve the compression rates, we address the later approach in this paper. We investigate a

variable rate compression method based on the variable block size segmentation of images. We focus our attention on Vector Quantization techniques [1][2], applied in the gray-level pixel space. As it will be shown, this kind of methods allows to perfoem at the same time compression and image feature extraction, and leads to content-based image indexing. Other interesting proposals with variable block size segmentation of images have been proposed in [3][4]. However the coding results are not so easyly linked to visual image features. II. VECTOR QUANTIZATION AND SOFM Images are divided into small n by n blocks, the source blocks. Each block is considered as a Ndimensional vector (with N=n2) in the åN vector space. Vector quantization is a mapping from åN into a finite subset Ω of åN, where Ω ={W1, ..,Wi, .. ,WM} is a set of predefined vectors, or blocks. These vectors Wi are called codewords and the set Ω is the vector quantization codebook. The distance between each source block X and the codewords is measured to select a representative codeword Wi for X. Then, only the label (or the index) of this codeword in the codebook is kept to create a compressed image. The main problem in this approach is the design of the codebook. Recently, the use of neural networks for codebook design problem has been investigated [5], particularly Kohonen's Self Organizing Feature Map (SOFM) [6], which is one of the most interesting neural networks for this kind of application. The main interesting properties of SOFM are: - self-organizing algorithm: it does not need to classify the training image blocks (unsupervised learning) - ability to form ordered topological feature maps: neighbouring neurons on the map have close weight vectors in the gray-level pixel space, i.e. they are visually similar. Therefore, it is possible to perform a classification of the image pixels by segmenting directly the SOFM. Another interest of these

topological properties is robustness against transmission errors [15]. - quantization is performed in the gray-level pixel space, and the visual aspect of images is preserved, which is very important for heavily textured images. III. IMAGE COMPRESSION AND QUADTREE Quadtree decomposition of images is often used to analyse and segment images according to chosen image features: homogeneity, texture parameters, etc... In the proposed scheme, we test the matching of codewords of different size with blocks of the image to encode. In our work, 4 codebooks having pixel blocks of size 16x16, 8x8, 4x4 and 2x2 are used. All the codebooks are generated independently using the SOFM learning algorithm, for creating maps of 16x16 neurons. Each of the four codebooks contains 256 codewords, in order to have one byte labels to design each codeword. Wherever it is possible in the image, large codewords will be used leading to high compression rate. Smaller blocks are used elsewhere, according to the quadtree decomposition of the image. The detailed procedure is the following. The image to be encoded is first divided into 16x16 pixel blocks. We look for the codeword of size 16x16 which is the closest to the studied block, in the sense of the Euclidean distance. If the distance between this block and the codeword is less than a predefined threshold, the block is coded by this codeword. If not, the 16x16 studied block is divided into four blocks of 8x8 pixels. The process is iterated, as many times as necessary, until the whole block is encoded, using 2x2 codewords if justified. Clearly, the thresholds are very important parameters, since they determine the tradeoff between compression rate and reconstruction quality. Low thresholds lead to low rates but good image quality, while using higher thresholds results in higher compression rates with lower compression quality. The algorithm is referred to as VQQT (Vector Quantization with a Quadtree scheme) and leads to a variable rate coding procedure, which includes side information describing the quadtree decomposition of the image [13]. IV. VQ AND IMAGE ANALYSIS Basically, VQ is used for image compression. However, this technique can be a useful tool for image analysis and characterization as well. In [14], we have introduced a new image descriptor, based on SOFM VQ refered to as "Signature Map" (SM). This image descriptor is a matrix [mij], which has

the same topology that the SOFM codebook. The value of each element mij of the SM is the number of occurrences, in the compressed image, of the codevector located at i,j in the SOFM (normalized 2D histogram of the codevectors used). We have shown in [14] that the SM provides interesting results in solving difficult tasks like as human face recognition. In this study we present a first investigation on the use of SM in medical image compression using VQQT. During compression process, one SM is created and associated to each codebook. Thus, if we use a 16-8-4-2 quadtree decomposition, 4 SMs will contain the probability density of each codeword in the compressed image. V. SIMULATION RESULTS The VQQT compression of 3 different medical image types is studied here (ultrasonic images of the esophagus, radiographic images of the hand and angiographic images of the retina.) Endoscopic Ultrasonography is an efficient tool in the detection and study of various gastrointestinal cancers. Angiographic images allow to detect retinal diseases. The hand images have been taken a classical X-ray image device. A. Compression results The VQQT approach was tested using 256 gray level images. The results were compared to the JPEG image compression standard in the sense of peak signal to noise ratio (PSNR) and visual reconstruction quality. Several echoendoscopic images of size 264x204 were used for creating 4 Kohonen codebooks with 16x16, 8x8, 4x4 and 2x2 codevectors. Each codebook contains 256 blocks. The results corresponding to different compression rates (obtained by thresholds adjustment) while compressing the image shown is Fig. 1 are reported and compared to JPEG in Table I. As we can see, the PSNR value provided by VQQT is always higher than that belonging to the JPEG. For visual comparison see Fig. 2 and 3. For the smallest compression rate, it is hard to see any noticeable difference. However, the JPEG image has a very poor quality for high compression rates, while VQQT still preserves important details. The main reason why is that JPEG applies a linear transform on fixed size (8x8) blocks, while VQQT adaptively determines the appropriate block size. Similar results were obtained when compressing angiographic images. Compression parameters were the same as above (i.e. codebook and block sizes),

but the images were slightly larger. The original 384x384 image is shown in Fig. 4. Compression performances are compared to JPEG in terms of rate/distortion in Fig. 5. In this case the objective distortion quality is better when using the JPEG method for lower rates, but VQQT outperforms JPEG when compressing at higher rates. Considering the subjective comparison presented in Fig. 6 and 7, the results are similar to the case of esophageal images, i.e. for high rates the VQQT provides significantly better quality than JPEG. We modified slightly the compression parameters for encoding radiographic hand images. These images are considerably larger (about 600x800 pixels). Hence we used 32x32, 16x16, 8x8 and 4x4 codewords, and the codebook sizes were 64, 64, 256 and 256, respectively. As we can see on the test image shown in Fig. 8a, a large part of the image is background. This can be efficiently encoded using large blocks, as shows the quadtree decomposition on Fig. 8b. Comparing to JPEG, the same comments can be made as for angiographic images. A compression example is illustrated on Fig. 8c. B. Signature Maps On Fig. 9 two compressed echoendoscopic esophagus images are shown with their SMs. On the SMs bright blocks illustrate high occurrences, while dark blocks correspond to low codeword occurrences. We observe that SMs are different for the two images, which suggest that the SM can be used as a simple image descriptor, as we shown for human face images. VI. CONCLUSION We have proposed a coding method which improves usual Kohonen's VQ, while keeping all of its main interests: ordered topological maps, coding in the gray-level pixel space to preserve the visual aspect of images for diagnosis. The drawback is the same as for all variable rate algorithms: sensitiveness to transmission errors. Among the variable rate compression techniques, we have shown that VQQT outperforms the JPEG standard for medical images of different, especially at high compression rates. Representing the image through known codewords allows to give a basic description of the image content, and to generate Signature Maps, which may be useful as well for diagnosis and for image indexing and retrieval. This use of the proposed compression scheme is under study, with improvements in codebook design, by taking into account image blocks content [8-12].

REFERENCES [1] N.M. Nasrabadi and R.A. King, "Image coding using vector quantization: a review", IEEE Trans. Com., Vol.36, pp. 957-971, Aug. 1988. [2] R.M. Gray, "Vector Quantization", IEEE ASSP Mag., pp. 4-29, Apr, 1984. [3] J. Vaisey, A. Gersho, "Variable rate image coding using quadtrees and vector quantization", Proc. EUSIPCO'88, Grenoble, France, september 1988 [4] J. Vaisey, A. Gersho, " Image Compression with variable Block Size Segmentation", IEEE Transactions on Signal Processing, Voll.40, No 8, Auggust 1992 [5] R.D. Dony and S. Haykin, "Neural network approaches to image compression", Proceedings of the IEEE, Vol.83, No.2, February 1995. [6] T. Kohonen, Self-Organization and associative Memory, 3d ed, 1989, Springer-Verlag. [7] H. Ritter, T. Martinez, K. Schulten, "Topology conserving maps for learning visuo-motor coordination", Neural Networks, Vol 2, 1989. [8] B. Solaiman, M.C. Mouchot and E. Maillard, "A hybrid Algorithm (HLVQ) combining unsupervised and supervised learning approaches", IEEE International Conference on Neural Networks, ICNN94, June 26-July 2, Orlando, USA 1994. [9] B. Solaiman, G. Cazuguel and C. Roux, "Compression d'images par l'algorithme HLVQ: comparaison des transformations utilisées pour guider l'apprentissage", Journées sur les nouvelles techniques pour la compression et la représentation des signaux audiovisuels, CNET, Grenoble, France,15-16 Fevrier, 1996. [10] A. Cziho, B. Solaiman, G. Cazuguel, C. Roux and I. Lovanyi, "Kohonen's self organizing features maps with variable learning rate. Application to image compression", 3nd International workshop on image and signal processing, pp. 11-14, 4-7 Novembre, 1996, Manchester, United Kingdom. [11] B. Ramamurthi, A. Gersho, "Classified vector quantization of images", IEEE Transactions on Communications, vol COM-34, november 1986 [12] A. Davignon, "Classification en blocs de taille variable pour codage d'image par quantification vectorielle", Traitement du Signal, 6(4), 1989 [13] G. Cazuguel, A. Cziho, B. Solaiman, C. Roux, M. Robaszkiewicz, "Improving Spatial Vector Quantization by use of a Quadtree Scheme. Application to Echoendoscopic Image Compression", Annual International Conference of the IEEE Engineering In Medicine and Biology Society, Chicago, USA, 1997 [14] A. Cziho, R. Ruiloba, G. Cazuguel, B. Solaiman, I. Lovanyi, C. Roux, "Content Based Image Indexing: Investigation Joined Image

Compression and Indexing by Use of SelfOrganizing Feature Maps", Magyar Kepfeldolgozok es Alalkferismerok Konferenciaja, 1997 [15] O. Aitsab, R. Pyndiah, B. Solaiman, "Joint optimization of multi-dimensional SOFM

Compression rate (CR) 17 23 35

VQQT

JPEG

29.47 dB 28.44 dB 27.16 dB

29.28 dB 27.97 dB 25.67 dB

codebooks with QAM modulations for vector quantized image transmission", 3rd International Workshop on Image/Signal Processing pp.3-6, Manchester, UK, 1996

Table I. VQQT and JPEG results for echoendoscopic image compression Fig. 1. Original echoendoscopic image

CR=17

CR= 23

CR= 35

Fig. 2. VQQT echoendoscopic image compression results with different compression rates (CR)

CR= 17

CR= 23

CR= 35

Fig. 3. JPEG echoendoscopic image compression results with different compression rates

Fig. 4. Original retinal image

Fig. 5. Comparison of the VQQT and JPEG performance for retinal image compression

CR=16

CR=60

Fig. 6. Retinal image compression with VQQT (CR : Compression Rate)

CR=16

Fig. 7. Retinal image compression with JPEG

CR=60

a. Original image

b. Quadtree decomposition

c. Compressed image

Fig. 8. Radiographic VQQT image compression example (CR=52, PSNR=35.59)

Image 1

Im 1 - 16x16 map

Im 2 - 16x16 map

Im 1 - 8x8 map Im 2 - 8x8 map

Image 2

Im 1 - 4x4 map

Im 2 - 4x4 map

Im 1 - 2x2 map Im 2 - 2x2 map

Fig 9. Signature maps of echoendoscopic images