Performance Analysis of Certain Scalar Wavelets on Mammogram Image Compression

Cancer is not just one disease, almost one hundred diseases. It is a disease of the genes. Breast cancer is in most incidence rates among women which cause cancer deaths ranking next to lung cancer. Breast cancer is treatable if detected early. Mammography is a special type of X-ray imaging used to create detailed images of the breast and also plays a major role in early detection of breast cancer. A high quality mammographic image is of high resolution but occupies large size. Hence, compression of mammogram images by preserving the information becomes utmost important. Clusters of fine, granular micro calcifications in mammograms may be an early sign of disease which may correspond to high correlation regions and curvilinear edges. Advances in wavelet transform are capable of surpassing the existing image compression standards like the Joint Photographic Experts Group (JPEG). Wavelet transform extracts the main signal co-efficient on all regions. The wavelet filters satisfying the desirable properties such as orthogonality and symmetry possess better compression. This paper uses certain scalar wavelets with different properties for mammogram decomposition and Set Partitioning in Hierarchical Tree (SPIHT) algorithm for compression. Few mammographic images from Mammogrphic Image Analysis Society (MIAS) database were tested using different families of wavelets with distinct properties and the results were analyzed. The wavelet satisfying the biorthogonal property gives better compression in many cases.