Generally, tissue extraction (segmentation) is one of the most challenging tasks in medical image processing. Inaccurate segmentation propagates errors to the subsequent steps in the image processing chain. Thus, in any image processing chain, the role of segmentation is in fact critical because it has a significant impact on the accuracy of the final results, such as those of feature extraction. The appearance of variant noise types makes medical image segmentation a more complicated task. Thus far, many approaches for image segmentation have been proposed, including the well-known active contour (snake) model. This method minimizes the energy associated with the target’s contour, which is the sum of the internal and external energy. Although this model has strong characteristics, it suffers from sensitivity to its control parameters. Finding the optimal parameter values is not a trivial task, because the parameters are correlated and problem-dependent. To overcome this problem, this paper proposes a new approach for setting snake’s optimal parameters, which utilizes an expertsegmented gold (ground-truth) image and an optimization algorithm to determine the optimal values for snake’s seven control parameters. The proposed approach was tested on three different medical image test suites: prostate ultrasound (33 images), breast ultrasound (30 images), and lung X-Ray images (48 images). In the current approach, the DE algorithm is employed as a global optimizer. The scheme introduced in this paper is general enough to allow snake to be replaced by any other segmentation algorithm, such as the level set method. For experimental verification, 111 images were utilized. In a comparison with the prepared gold images, the overall error rate is shown to be less than 3%. We explain the proposed approach and the experiments in detail.
Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen