Perceptual mass segmentation using eye-tracking and seed-growing

Abstract

In the paper, we propose a novel scheme for breast mass segmentation in mammography, which is based on visual perception and consists of two steps. Firstly, radiologists' eye-gazing data is recorded by the eye-tracker during reading and then clustered with a density-based spatial clustering of applications with noise (DBSCAN) algorithm to achieve seeds locating radiologists' regions of interest (ROIs). The seeds-based region growing (SBRG) algorithm is applied to buckle ROIs containing suspicious lesions. Secondly, in order to achieve fine lesion contour as final result, the ROIs are segmented with a multi-scale mass segmentation approach using active contour models. The result of applying the proposed method to the mammograms from both DDSM and Zhejiang Cancer Hospital shows that the achieved average of overlap rate is 0.5915 and the achieved average of misclassification rate is 0.6342. The innovative point of the proposed approach is to introduce visual perception into breast mass segmentation, which makes the result of mass segmentation meet radiologists' subjective demand.