IMAGE COMPRESSION USING K-MEANS CLUSTERING AND NUCLEAR MEDICINE IMAGE PROCESSING
|Himadri Nath Moulick1, Moumita Ghosh2
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In computer vision, image compression mainly refers to the problem of reducing the memory size of a digital image. Mainly problems regarding data transferring over internet might require an image data to be of comparatively lesser memory size. Moreover high bandwidth is also required for transmission of high quality image data. Image compression provides solution to this problem. Now in order to retrieve the image of lesser size there is a considerable amount of loss of image data. For this problem optimized method is chosen to provide a final image which is comparatively of smaller memory size than the original image, yet it is quite visually similar to the original image. In this paper, we are trying to segment the original image using K-Means clustering method. Let us consider a grey level image of M XN size, now we know there are in total 0-255 grey level intensity values i.e 8 bits for each pixel therefore the total size of the image will be M XN X8 bits. Now in our approach we have segmented the image into K clusters. Let the set of cluster centres be (c1, c2,..........., ck) i.e. we have seen that on segmentation of the image into 8 (k=8) segments better results are obtained, hence the total image can be replaced accordingly with 8 grey level intensity values, so the 8 values can be represented by 3 bits (000, 001, ……..,111). Now if we represent the image with 8 grey level values there is a high loss of data. In our approach we have tried to minimize the loss of data using correlative coefficient function. In every iteration of K-Means clustering algorithm we have compared the segmented image with the original image until the loss of data is minimized (the value of correlative co-efficient function should be maximum) and then the iteration is stopped. In this approach the compressed image is of M XN X3bits memory size, hence there is 62.5% decrease of the memory size of the compressed image, but still there is above 90% visual similarity of the compressed image with the original image. So in our approach the main bottleneck of image compression is satisfied considerably. Advanced techniques of image processing and analysis find widespread use in medicine. In medical applications, image data are used to gather details regarding the process of patient imaging whether it is a disease process or a physiological process. Information provided by medical images has become a vital part of todayÃƒÂ¢Ã‚Â€Ã‚ÂŸs patient care. The images generated in medical applications are complex and vary notably from application to application. Nuclear medicine images show characteristic information about the physiological properties of the structures-organs. In order to have high quality medical images for reliable diagnosis, the processing of image is necessary.