Contrast Enhancement of Mammograms and Microcalcification Detection
S.Anand1, J.Murugachandravel2, K.Valarmathi3, Abhisha Mano4, N.Kavitha5
1S. Anand*, Professor, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
2J.Murugachandravel, Assistant Professor (Sl. Grade), Department of Master of Computer Application, Mepco Schlenk Engineering College, Sivakasi, India.
3K.Valarmathi, Senior Lecturer, Department of Electronics and Communication Engineering, S.Vellaichamy Nadar Polytechnic College, Virudhunagar, India.
4Abhisha Mano, Asst. Professor, Department of Electronics and Communication Engineering, Rajas International Institute of Technology for Women, Nagercoil, India.
5N.Kavitha, Assistant Professor, Department of Computer Science Engineering, Saranathan Engineering College, Tiruchirapalli, India.

Manuscript received on November 10, 2019. | Revised Manuscript received on November 17, 2019. | Manuscript published on 30 November, 2019. | PP: 3926-3929 | Volume-8 Issue-4, November 2019. | Retrieval Number: F2473037619/2019©BEIESP | DOI: 10.35940/ijrte.F2473.118419

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Abstract: Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.
Keywords: Contourlet; Edge Information; Hyperbolic Tangent filter; Mammogram Image Enhancement.
Scope of the Article: Neural Information Processing.