A Hybrid System for Detection of Masses in Digitized Mammograms

In this paper, a hybrid segmentation method for detection of masses in digitized mammograms has been developed using three parallel approaches: adaptive thresholding method, Gabor filtering and fuzzy entropy feature as a CAD scheme. The algorithm consists of the following steps: a) Preprocessing of the digitized mammograms including identification of region of interest (ROI) as candidate for massive lesion through breast region extraction, b) Image enhancement using linear transformation and subtracting enhanced from the original image, c) Characterization of the ROI by extracting the fuzzy entropy feature, d) Local adaptive thresholding for segmentation of mass areas, e) Filtering the input images using Gabor functions, f) Combine expert of the last three parallel approaches for mass detection. The proposed method was tested on 78 mammograms (30 normal & 48 cancerous) from the BIRADS and local databases. The detected regions validated by comparing them with the radiologists’ handsketched boundaries of real masses. The current algorithm can achieve a sensitivity of 90.73% and specificity of 89.17%. This approach showed that the behavior of local adaptive thresholding, Gabor filters and fuzzy entropy technique could be useful for mass detection on digitized mammograms. Our results suggest that the proposed method could help radiologists as a second reader in mammographic screening of masses.