Open AccessContrast Enhancement of Mammograms and Microcalcification Detection
Author(s) -
S. Anand,
J. Murugachandravel,
K. Valarmathi,
Abhisha Mano,
N. Kavitha
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f2473.118419
Subject(s) - contourlet , microcalcification , artificial intelligence , adaptive histogram equalization , contrast (vision) , histogram equalization , computer vision , mammography , edge enhancement , computer science , pattern recognition (psychology) , histogram , edge detection , enhanced data rates for gsm evolution , mathematics , breast cancer , image enhancement , wavelet transform , image (mathematics) , wavelet , image processing , medicine , cancer
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.