Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.

The presence of predominant density region of the pectoral muscle in Medio-Lateral Oblique (MLO) view of the mammograms can affect or bias the results of mammograms processing for breast cancer detection using intensity based methods. Therefore, to improve the diagnostic performance of breast cancer detection using computer-aided system, identification and segmentation of pectoral muscle is an important task. This paper presents, an intensity based approach to identify the pectoral region in mammograms. In the presented approach enhancement mask and threshold technique is used to enhance and select the pectoral region and boundary points respectively, to find the boundary of pectoral muscle. Then curve fitting by Least Square Error (LSE) method is used to refine the rough initial boundaries. The proposed approach was applied on 320 mammograms from mini-Mammographic Image Analysis Society (mini-MIAS) database of 322 mammograms, with acceptable rate of 96.56% from radiologist experts. The performance evaluation for pectoral muscle segmentation, based on Hausdorff distance (H d ), False Positive (FP) and False Negative (FN) rate, shows the usefulness and effectiveness of the proposed approach.

Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.