Over people's lifetimes, the prevalence of shoulder pain exceeds 70%. In particular, 70% of shoulder pain is caused by the rotator cuff lesions that are located in the supraspinatus area. The automatic and quantitative segmentation of the supraspinatus area can provide a more-objective and accurate assessment of the rotator cuff lesions. In this paper, 108 shoulder ultrasound images comprised the image database to evaluate the proposed segmentation method, and a multilayer self-shrinking snake (S 3), based on a multilayer segmentation framework, was used to achieve optimal segmentation. Using a rough initial contour that enclosed the supraspinatus area, the modified snake was shrunken with an iteration procedure according to the boundary conditions that included the elasticity, curvature, gradient, and distance. In the performance evaluation, S 3 achieved an F-measure of 0.85. The success of S 3 could provide more-objective location information to physicians diagnosing rotator cuff lesions.
Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.
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