D.-S. Huang, K. Li, and G.W. Irwin (Eds.): ICIC 2006, LNCS 4113, pp. 337 – 344, 2006. © Springer-Verlag Berlin Heidelberg 2006

Compression of Medical Images by Using Artificial Neural Networks

Zümray Dokur

Department of Electronics and Communication Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey

zumray@ehb.itu.edu.tr

Abstract. This paper presents a novel lossy compression scheme for medical images by using an incremental self–organized map (ISOM). Three neural net-works for lossy compression scheme are comparatively examined: Kohonen map, multi-layer perceptron (MLP) and ISOM. In the compression process of the proposed method, the image is first decomposed into blocks of 8×8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are com-puted for each block. The dimension of DCT coefficients vectors (codewords) is reduced by low-pass filtering. Huffman coding is applied to the indexes of codewords obtained by the ISOM. In the decompression process, inverse opera-tions of each stage of the compression are performed in the opposite way. It is observed that the proposed method gives much better compression rates.

1 Introduction

Medical images like magnetic resonance (MR) and computer tomography (CT) ac-quired from various modalities comprise huge amounts of data, rendering them im-practicable for storage and transmission. Archiving this large amount of image data in the computer memory is very difficult without any compression. An important issue in lossy compression of medical images is the risk of destroying diagnostically rele-vant information. Current lossy compression standards, such as JPEG [1] and MPEG, are designed for conventional still-image and video display.

Transform-based techniques have been proposed for the efficient reduction of the high redundancy usually encountered in real life images [2]. Unsupervised neural networks can perform nonlinear principal component analysis as a transform-based method in image compression [3]. They outperform linear principal component analy-sis, and are relatively easy to implement.

Another common method to compress images is to code them through vector quan-tization (VQ) techniques. Self-organized Kohonen maps have been used to achieve the VQ process of image compression [4]. They represent an efficient compression scheme based on the fact that consecutive blocks in an image are often similar, and thus coded by similar codewords with a VQ algorithm. Early studies of lossy com-pressed medical images performed compression using variations on the standard discrete cosine transform coding algorithm combined with scalar quantization and lossless coding. More recent studies of efficient lossy image compression combined with scalar or vector quantization [5]. These algorithms provide several potential

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advantages over traditional Fourier-type decompositions, including better concentra-tion of energy, better decorrelation for a wider class of signals. The wavelet-based approach, however, has a filter whose length varies as a function of resolution.

This paper presents a novel compression scheme which is a fusion of DCT [6] of the original image and vector quantization by the ISOM.

2 Method

In the study, the medical image is splitted into square blocks of 8×8 pixels. 2D-DCT coefficients are computed for each block. By using the DCT, energy of the image in the square block is compressed into the low frequency band. 2D-DCT coefficients of an 8×8 image block are shown in Fig. 1. Coefficients in dark-colored regions repre-sent the details of the image in the block. In the compression algorithm, some of these coefficients are ignored while the others are stored into the memory. In the proposed method, four coefficients (C11, C12, C21 and C22) which are shown inside a bold-bordered square are used to form the codewords. The reconstructed image quality depends on the amount of ignored coefficients. The more coefficients we use in the compression, the better image we can obtain in the reconstruction process.

C11 C12

C21 C22

C88

...

...

.... . . .. . . .. . . .

C18

Fig. 1. 2D-DCT coefficients of an image block of 8×8 pixels

The lossy compression scheme is described in Fig. 2(a). After splitting the image into square blocks, 2D-DCT coefficients are computed. Then, a low-pass filter is used to eliminate high frequencies not visible to human eyes. By removing a part of the high frequencies, a reduction in the peak-signal-to-noise-ratio is achieved, even though the image visual quality remains more or less unchanged. In other words, before the compression, image quality will be degraded by the filtering. The output of the low pass filter is applied to a neural network for the vector quantization process. At this stage, a single index for each image block is generated by the proposed neural network. At the last stage, Huffman Coding is applied to the indexes of codewords.

The decompression scheme is illustrated in Fig. 2(b). At first, the indexes of the codewords are obtained by the inverse Huffman coding. Then, the corresponding codewords for these indexes are generated from the look-up table. The square image blocks are reconstructed by applying inverse 2D-DCT to the codewords. At the last stage, the original image is reconstructed from these blocks.

Compression of Medical Images by Using Artificial Neural Networks 339

Fig. 2. (a) Compression scheme, (b) decompression scheme

3 Artificial Neural Networks

Neural networks are frequently used in biomedical signal processing. One of the ap-plication areas of neural networks is data compression [7, 8].

In this study, three neural networks are comparatively examined for the compres-sion of medical images: Kohonen map [9], MLP, and ISOM. Kohonen map and ISOM are used in the method explained by Fig. 2. But, the method in which MLP is used is different from the proposed one. Pixel intensities in region of interest are di-rectly presented to MLP without any transformation. MLP is designed with three layers. The first and the second layers are used in the compression scheme while the third layer is used in the decompression. Fig. 3 shows the structure of the network. Pixel intensities in 5×5 image blocks are directly fed into the MLP (no preprocessing is performed on pixel intensities).

3.1 Incremental Self-Organized Map

ISOM is a two-layer network as shown in Fig. 4. In the figure, k represents the dimen-sion of the DCT coefficients vectors reduced by the low-pass filter. The DCT coeffi-cients vectors with reduced dimension are called as codewords. Codewords are pre-sented to the input of the ISOM. The nodes in the first layer are automatically deter-mined during the training. After the training is completed, the nodes represent code-words distributed homogeneously in the feature space. The winner-takes-all guaran-tees that there will be only one node activated. Index layer represents the index values of the codewords.

3.2 Training of the ISOM

ISOM is a neural network that does not update weights. It may also be called as an adaptive vector quantizer. ISOM has incremental structure for unsupervised learning. The nodes in the first layer represent the codewords formed by 2D-DCT transform.

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Fig. 3. Structure of the MLP used in compression and decompression schemes

Index layer

First layer

……..

……..

growing directionW I N N E R - T A K E S - A L L

C1 C2 Ck

Fig. 4. Structure of ISOM

At the beginning, the codeword of the first image block is automatically selected as the first node of the ISOM. Then, the second block is extracted from the image. After the DCT coefficients of this block are computed, a low-pass filter is used to eliminate some amount of high frequency content. Thus, the dimension of the vectors is reduced to increase the compression rate. The distance between the codeword and the first node in the first layer of the ISOM is computed. If the distance exceeds a pre-determined threshold value, the codeword is assigned as a new node of the ISOM and a new index is assigned for this codeword. Otherwise, the third image block is ex-tracted and search is continued until all blocks in the image are exhausted.

The training process of the ISOM can be summarized as follows:

Step 1: Extract a square block from the image in an order. Step 2: Compute the DCT coefficients of the image in the block to form a

codeword. Use the low pass filter to reduce the dimension of the DCT

Compression of Medical Images by Using Artificial Neural Networks 341

coefficients vectors. Present the reduced dimensional vector (the codeword) to the ISOM.

Step 3: Compute the distances between the codeword and the node(s) in the first layer of the ISOM, and find the minimum distance.

Step 4: If the minimum distance exceeds the threshold, include the codeword into the ISOM as a new node, increment the index counter by one, and assign the value in the counter as the ‘index’ of the codeword. Send this index to Huff-man coding process. Otherwise, send the index of the node which is the near-est to the codeword, to Huffman coding process.

Step 5: Go to step 1 until all blocks in the image are exhausted.

4 Computer Simulations

In this study, MLP, Kohonen map, ISOM and JPEG standard are comparatively ex-amined for medical image compression. MR, CT and ultrasound images are com-pressed by these four methods. The original medical images are shown in Fig. 5. The size of MR and CT head images is 256×256, and the size of the ultrasound image is 400×288. All simulations are performed by using MATLAB toolbox.

There are three control parameters in the compression processes realized by the Kohonen map and ISOM: (i) Size of the square blocks, (ii) dimension of the DCT coefficients vectors (codewords) determined by the low-pass filtering process, and (iii) the number of nodes in the neural network.

The size of the square blocks affects the compression rate. If the size is high, com-pression rate increases, however, block effects and edge distortions are observed in the reconstructed image. In the study, the size of the square block for each network is different from each other. Low-pass filter sets high-order 2D-DCT coefficients to zero value. By ignoring zero-valued coefficients, the dimension of the codeword is de-creased, and the ignored coefficients are not presented to the neural networks. How-ever, high-order DCT coefficients represent the details in the image. Thus, the amount of ignored coefficients will affect the quality of the reconstructed image.

Table 1 shows mean square error (MSE), compression rate (CR) and number of nodes for the compression methods by artificial neural networks and JPEG. CR for MLP is determined as follows:

CR = number of nodes in input layer / number of nodes in the second layer (1)

In the Kohonen map and ISOM, the size of the compressed image (SOCI) and the CR are calculated as follows:

SOCI = Size of look-up table + Size of indexes compressed by Huffman coding CR = Image size / SOCI (2)

where, look-up table contains codewords which are assigned as the nodes of the net-works. CR value depends on some parameters: (i) (the size of the square block) / (the size of the codeword), (ii) (the size of look-up table) × 8 (bayts), and (iii) performance of the Huffman coding.

The best results obtained in 50 independent runs for each image are given in the table. The best and the average MSE values of ISOM and Kohonen map are similar

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for 50 independent runs. However, the average MSE value of MLP is higher than the best (lowest) MSE value for 50 independent runs. This is because of the fact that MLP is easily caught by local optimum solutions.

Because the size of the square block for MLP is determined as 5×5 after several tri-als, training sets of MLP for MR, CT and ultrasound images consist of 2621, 2621 and 4608 square blocks, respectively. The size of the square block for Kohonen map is selected as 8×8 after several trials. Thus, the training sets of the Kohonen map for MR, CT and ultrasound images consist of 1024, 1024 and 1800 square blocks, respec-tively. Dimension of the codewords is selected as 4×4.

(a) (b) (c)

Fig. 5. (a) MR head image, (b) CT head image, (c) Ultrasound image of kidney cyst

Table 1. Performance results obtained by artificial neural networks and JPEG

Method Images MSE Number of nodes CR

MR 82.481 25−15−10−25 2.5 CT 43.561 25−20−10−25 2.5

by MLP

US 98.274 25−10−10−25 2.5 MR 51.371 263 35.386 CT 19.843 160 63.015

by ISOM

US 133.019 304 38.193 MR 52.068 26.131 CT 19.958 29.735

by JPEG

US 138.221

37.586 MR 158.873 22 × 22 7.670 CT 64.532 22 × 22 7.672

by Kohonen map US 71.665 25 × 25 9.020

MSE: Mean square error; CR: Compression rate

Fig. 6 shows the three reconstructed images obtained by the ISOM. As the size of the square blocks for ISOM is selected as 8×8 after several trials, training sets of ISOM for MR, CT and ultrasound images consist of 1024, 1024 and 1800 square blocks, respectively. Dimension of the codewords is selected as 2×2. The threshold value is selected as 3000 for each medical image.

Fig. 7 shows the reconstructed images obtained by the JPEG standard.

Compression of Medical Images by Using Artificial Neural Networks 343

(a) (b) (c)

Fig. 6. Reconstructed images by the ISOM

(a) (b) (c)

Fig. 7. Reconstructed images by the JPEG

5 Conclusions

In Table 1, the parameters of the methods are adjusted so that the MSE values of JPEG standard and ISOM are the same. CRs of the two methods are comparatively examined for approximately the same MSE values.

The CR obtained by using MLP is 2.5. The different structures of the MLP which gave the results in Table 1 are determined after several trials. Each training process for a different trial took approximately half an hour. It is observed that if the CR is increased, the quality of the reconstructed image highly decreases. The CR is con-trolled by two parameters: (i) Block size, (ii) the number of nodes in the second layer. If the size of the square blocks is increased, block effect and edge distortion are ob-served in the reconstructed image. The number of nodes in the second layer is deter-mined after 50 different trials. A satisfactory solution could not be found for less than 10 nodes (M=10 in Fig. 3) in the second layer. The number of nodes affects the com-pression rate and the quality of reconstructed image. If the number of nodes is high, compression rate will decrease and the reconstructed image quality will increase.

In the Kohonen map, the number of nodes has to be determined before the compression process. If the number of nodes becomes extremely high, the CR will decrease, but the quality of the reconstructed image will increase. Before the com-pression process is started, training of the Kohonen map is realized. For this reason, the compression process consumes extra time to learn the map.

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In ISOM, the number of nodes is determined automatically during the compression by using a threshold. Thus, no extra time is consumed for the training process. The threshold value is selected as 3000 for the three medical images. The quality of the reconstructed image is determined by the threshold. A low threshold value leads to an excessive number of nodes in the first layer, and a good representation of the image. On the contrary, a high value may lead to a poor representation of the image.

In the study, noise is removed from the images by selecting low-order DCT coeffi-cients. Thus, noise does not affect ISOM. MR and CT images are generally noise-free images. However, ultrasound images have textural content which can be characterized by white noise. Hence, as presented in Table 1, the highest MSE value is obtained for the ultrasound image by the ISOM network.

It is observed that the proposed method gives higher CRs with low MSE. The qual-ity of the reconstructed images by the ISOM is higher than the quality of the images reconstructed by the other methods.

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