Research Specifications

Digital transformation has brought radical changes in several domains. Particularly, image processing techniques have been generally used in medical, security, and monitoring applications. Image segmentation is a specific task where an image is partitioned in meaningful segments, containing similar features and properties. Its aim is to simplify the original image for easy analysis since relevant information is highlighted. These techniques are commonly used to support medical experts in detecting areas of interest in medical images. Level set method is a methodology for image segmentation, which works with minimizing energy for segmentation of the image by active contours. The areas inside each contour belong to distinct segments. In active contour-based models, the level of each contour changes according to the intensity values (region-based active contours) or the gradient variations (edge−based active contours). Here, a new edge–region level set algorithm for image segmentation is proposed which controls the curve movement based on both intensity and gradient values. Moreover, the original active contour model has been modified by considering both the mean and the variance values of the pixels’ neighborhood, instead of the mean value only. Indeed, in homogeneous regions with the same mean value could be assigned to the same segment while belonging to different ones. Since the initial curve definition is crucial for level set methods, a new methodology for initial curve detection based on Canny edge detector has been proposed. Experiments have been conducted on brain tumor magnetic resonance imaging (MRI). Images from Whole Brain Atlas (Harvard University Medical School) datasets, part Neoplastic Disease (brain tumor) have been used. Results have shown that the suggested approach is able to accurately detect tumor regions in the images and to overcome the original active contour models such as CV, LBF, and LIF. Using semiaverage filter in pre-processing stage can