Abstract: The huge growth of the usage of internet increases the need to transfer and save multimedia files. Mammogram images are part of these files that have large image size with high resolution. The compression of these images is used to reduce the size of the files without degrading the quality especially the suspicious regions in the mammogram images. Reduction of the size of these images gives more chance to store more images and minimize the cost of transmission in the case of exchanging information between radiologists. Many techniques exists in the literature to solve the loss of information in images. In this paper, two types of compression transformations are used which are Singular Value Decomposition (SVD) that transforms the image into series of Eigen vectors that depends on the dimensions of the image and Discrete Cosine Transform (DCT) that covert the image from spatial domain into frequency domain. In this paper, the Computer Aided Diagnosis (CAD) system is implemented to evaluate the microcalcification appearance in mammogram images after using the two transformation compressions. The performance of both transformations SVD and DCT is subjectively compared by a radiologist. As a result, the DCT algorithm can effectively reduce the size of the mammogram images by 65% with high quality microcalcification appearance regions.

Ayman AbuBaker, Mohammed Eshtay and Maryam AkhoZahia, “Comparison Study of Different Lossy Compression Techniques Applied on Digital Mammogram Images” International Journal of Advanced Computer Science and Applications(IJACSA), 7(12), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071220

@article{AbuBaker2016,
title = {Comparison Study of Different Lossy Compression Techniques Applied on Digital Mammogram Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071220},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071220},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {12},
author = {Ayman AbuBaker and Mohammed Eshtay and Maryam AkhoZahia}
}