Medical Image Compression Using Fuzzy C-Means Based Contourlet Transform

Abstract

Problem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the efficient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image applied with Fuzzy C-means based contourlet transform. DWT applied to the rest of the image. Finally modified EZW of six symbols differing from normal EZW was applied to the whole image. This technique increases PSNR and gives better compression ratio. Results: The MATLAB simulation showed that the method of separate transforms to the two regions proves better results compared to the ordinary way of applying only single transforms to the whole image. The results revealed that proposed algorithm was simple and computationally fewer complexes based on embedded block coding with coefficient truncation. Conclusion:The compression of the proposed algorithm is superior to EZW, SPIHT. Our new method of compression algorithm can be used to improve the performance of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical images.

Self Organizing Map Cluster Approach for Wavelet Based Medical Image Compression

“…Three images of 256×256 pixels in size are used. It can be seen that SOM outperforms FCM (Tamilarasi and Palanisamy, 2011) and FKM. The algorithms are evaluated in the terms of CR and PSNR.…”

Multi‐level fuzzy contourlet‐based image fusion for medical applications

“…Contourlet transform is now the most widely used method at the pixel‐level fusion because it offers a flexible multi‐resolution and directional decomposition for images, since it allows for different number of directions at each scale [17, 18]. The contourlet transform consists of two steps: first, the images are decomposed by Laplacian Pyramid (LP) into one low‐frequency sub‐band and various high‐frequency sub‐bands (LP is used to confine edge point discontinuities).…”

“…To improve the fusion accuracy, the region statistics idea based on contourlet transform is integrated in the fusion rule. For a complete mathematical foundation of contourlet transform, readers can refer to [18].…”

“…In general, image region boundaries are a basic element in the perception of image structure. Recently, fuzzy clustering is used to separate out interesting areas and generates fuzzy partitions of the data instead of hard partitions [18, 19]. In this case, data patterns may belong to several clusters, having different membership values with different clusters.…”

“…In order to resolve the problem of uncertainty contribution of each source’ image to the fused image and integrate as much information of each source as possible, a lot of state‐of‐the‐art researches have adapted fuzzy reasoning to handle the drawbacks of traditional fusion algorithms [8–12, 18]. With the help of fuzzy if–then rules and membership functions designed for the image data set, the fuzzy logic approach can model and combine the images to enhance the contrast of the fused image.…”