An adaptive level set method for improving image segmentation

Segmentation is one of the most significant techniques in image processing studies. But the major limitations of low contrast, ambiguous boundary and complex morphology of images usually lead to unsatisfactory segmentation, especially in heavily noisy medical images. To address this problem, an attempt by improving Distance Regularized Level Set Evolution (DRLSE) model has been made to build up an effective and accurate active contour model to improve the segmentation ability. In this study, a balance concept between the pushing force and image’s contour strength of DRLSE model was adapted for contour segmentations, so the contour convergent/divergent force will be re-defined to make it more feasible for complicated boundaries. The proposed concept integrates the bilateral filtering and Canny contour, and the improved edge indicator function into the DRLSE model. The most important step is to adapt the improved edge indicator function appropriately with the Canny contour. To verify our proposed DRLSE scheme, different testing images are used. The experimental results show that the proposed approach has been examined and tested successfully in most cases. Consequently, the developed algorithm including the DRLSE model combing the Canny contour with an improved g edge indicator, demonstrate the effectiveness and reliability, especially in the testing samples with weak edges and complex topologies.