Computerised segmentation of suspicious lesions in the digital mammograms
Zainul Abdin Jaffery, Laxman Singh · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2014
In this paper, a robust marker-controlled watershed method is proposed to yield more accurate segmentation results to delineate the masses in mammograms. The proposed method consists of three main steps: pre-processing, marker extraction and the final segmentation. In the first step, pre-processing algorithm is developed using top-hat morphological filter, wavelet transform followed by a noise smoothing anisotropic diffusion filter. In the second step, a novel technique is developed for the extraction of robust markers to locate the accurate position of the suspicious lesions. Finally, the extracted markers are used within the watershed algorithm to allow the reliable segmentation and quantification of masses in mammograms. The developed computer method was quantitatively evaluated using the area overlap metric (AOM), average minimum Euclidean distance (AMED) and Hausdorff distance (HD). The mean ± standard deviation values of AOM, AMED and HD for our method are 0.83 ± 0.10, 1.49 ± 1.20 mm and 4.62 ± 0.80 mm. We compared our method with previously developed marker-controlled watershed algorithm with respect to the manual segmentation performed by an expert radiologist. Experimental results demonstrate that our method has a strong potential to be used as an aid to radiologists in the interpretation of screening mammograms.