Relevant edge probability-based adaptively weighted active contour for medical image segmentation

The level-set based active contours have been found popular for medical image segmentation tasks, because of their inherent support for the topological changes-splitting and merging. Meanwhile, contour's leakage through the weak edges and premature convergence due to intensity inhomogeneity diminish its accuracy. Adjusting energy weights according to image features, local to the contour can be helpful. However, weight adjusted as deterministic function of the features is not adequate, limiting the segmentation accuracy. To address the problem, a new relevant edge probability based adaptively weighted level-set evolution (REP-WLSE) method for medical image segmentation is investigated. The weights used in this proposal are adaptive to an image relative value, obtained statistically from the feature-explorations during the contour's evolution. The value is basically an estimate of contour's probability of finding relevant boundary edges on the image plane. Spatial intensity-range filtering provides the feature space. An adaptive time-step management scheme is also implemented, which controls the speed variation of the contour evolution. Time-step is adjusted as a function of contour's alignment with the edges. The merits of the suggested methodology are-(i) reduced leakage through the weak edges, (ii) ability to handle the inhomogeneity, and (iii) increased chances of convergence around the foreground/region of interest (ROI). Experimental results using brain MR, abdominal ultrasound, and breast ultrasound images are presented. State-of-the-art methods are compared using different metrics. The suggested methodology achieved better results.

Automated summary

In this study, a new adaptively weighted level-set evolution method based on relevant edge probability is investigated for medical image segmentation. By adjusting the weights according to the image's relative value, the leakage and premature convergence are reduced, leading to improved segmentation accuracy.