A REVIEW ON COMPUTER AIDED MAMMOGRAPHY FOR BREA ST CANCER DIAGNOSIS AND CLASSIFICATION USING IMAGE MINING METHODOL OG Y

A REVIEW ON COMPUTER AIDED MAMMOGRAPHY FOR BREAST CANCER DIAGNOSIS AND CLASSIFICATION USING IMAGE MINING METHODOLOGY Aswini Kumar Mohanty1, Pratap Kumar Champati2, Sukanta Kumar Swain3 and Saroj Kumar Lenka4 1PHD Scholar, SOA University, Bhubaneswar, Orissa, India E-mail: asw_moh@yahoo.com 2Deptt. Comp. Sc,. ABIT, Cuttack, Orissa, India E-mail: pratapchampati@yahoo.com 3Sukanta Kumar Swain, NIIS, Madanpur, Bhubaneswar, Orissa, India E-mail: suka_jul01@yahoo.co.in 4Mody Univesity, Department of Comp Sc, Laxmangargh, Rajstan, India E-mail: lenka.sarojkumar@gmail.com Image mining focuses finding unusual patterns in images and deals with making association between different images from large image database. It deals with the extracting inherent and embedded knowledge, image data relationship, or other patterns and which is not explicitly found in the images. It is more than just an expansion of data mining to image domain, where as image processing deals with detection of abnormal patterns as well as retrieving images. Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is based on the research, which investigated the state of art of computer aided detection systems for digital mammograms, and evaluated the related techniques in image pre-processing, feature extraction and classification of digital mammograms. Furthermore, this paper explored the further research directions for next generation CAD for mammograms. It was identified that computer-aided detection techniques for masses and microcalcifications have been extensively studied, but the detection techniques for architectural distortion and asymmetry in mammograms still are challenges.

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