Open AccessA combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain
Author(s) -
S. P. Srivastava,
Neeraj Sharma,
Sanjay Kumar Singh,
Rajeev Srivastava
Publication year - 2014
Publication title -
journal of medical physics/journal of medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.292
H-Index - 24
eISSN - 1998-3913
pISSN - 0971-6203
DOI - 10.4103/0971-6203.139007
Subject(s) - artificial intelligence , pattern recognition (psychology) , adaptive histogram equalization , thresholding , computer science , segmentation , unsharp masking , discrete wavelet transform , mathematics , computer vision , wavelet , histogram , histogram equalization , wavelet transform , image processing , image (mathematics)
In this paper, a combined approach for enhancement and segmentation of mammograms is proposed. In preprocessing stage, a contrast limited adaptive histogram equalization (CLAHE) method is applied to obtain the better contrast mammograms. After this, the proposed combined methods are applied. In the first step of the proposed approach, a two dimensional (2D) discrete wavelet transform (DWT) is applied to all the input images. In the second step, a proposed nonlinear complex diffusion based unsharp masking and crispening method is applied on the approximation coefficients of the wavelet transformed images to further highlight the abnormalities such as micro-calcifications, tumours, etc., to reduce the false positives (FPs). Thirdly, a modified fuzzy c-means (FCM) segmentation method is applied on the output of the second step. In the modified FCM method, the mutual information is proposed as a similarity measure in place of conventional Euclidian distance based dissimilarity measure for FCM segmentation. Finally, the inverse 2D-DWT is applied. The efficacy of the proposed unsharp masking and crispening method for image enhancement is evaluated in terms of signal-to-noise ratio (SNR) and that of the proposed segmentation method is evaluated in terms of random index (RI), global consistency error (GCE), and variation of information (VoI). The performance of the proposed segmentation approach is compared with the other commonly used segmentation approaches such as Otsu's thresholding, texture based, k-means, and FCM clustering as well as thresholding. From the obtained results, it is observed that the proposed segmentation approach performs better and takes lesser processing time in comparison to the standard FCM and other segmentation methods in consideration.