Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes

Mammogram image visual enhancement, mass segmentation and classification

“…Several methods are used to manipulate mammogram images, and can be categorized into four main groups; conventional enhancement techniques, region-based enhancement techniques, feature-based enhancement techniques, and fuzzy enhancement techniques. Conventional enhancing techniques appear in the works of [8,3,49,43,27,65,29] are fixed neighborhood techniques and are used to modify images based on their global properties. Region-based methods, as in [23,47], are used for enhancing the contrast of mammogram features according to the surrounding pixels.…”

“…Clustering segmentation methods have been employed in [50,42,59,45,11,61,60,14,64]. Regions of interest segmentation using a single view and the multiple views are used to segment both masses and calcifications, those methods have been used in the work of [49,29,23,51,37,58,66,10,40,53,39,9,15,18]. Graph segmentation methods can also be used to segment masses, and are applied in [4,33,34,20,22,21,67].…”

Automatic Diagnosis of Breast Tissue

“…Second, using the border points and nipple position as the reference of mammogram images are aligned and subtracted to extract the suspicious region. In the context of detection ROI, Schiabel et al (Schiabel et al, 2008) proposed a methodology based on the Watershed transformation, which is combined with two other procedures; histogram equalization, working as pre-processing for enhancing images contrast, and a labelling procedure intended to reduce noise. But, Jadhav et al (Jadhav & Thorat, 2009) used statistical feature extraction method by using a sliding window analysis, for detecting circumscribed masses in mammograms.…”

Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

“…Second, using the border points and nipple position as the reference of mammogram images are aligned and subtracted to extract the suspicious region. In the context of detection ROI, Schiabel et al [8] proposed a methodology based on the Watershed transformation, which is combined with two other procedures: histogram equalization, working as pre-processing for enhancing images contrast, and a labelling procedure intended to reduce noise. However, the method based on fuzzy region growing is illustrated in [9].…”