A novel edge detection approach on active contour for tumor segmentation

Active contour methods for image segmentation allow a contour to deform iteratively to partition an image into various regions. A novel region-based active contour model proposed by Kaihua Zhang which take advantages of both geodesic active contours and Chan-Vese model is discussed in this paper. This method uses region-based signed pressure force (SPF) to stop the contours at weak or blurred edges. In practise, this method is useful only for images with clear boundary. Second, this method is strongly depends on parameter α which controls the propagation of moving curve. α value depends on size of region to be extracted. There is no fixed α value which works well for all images. In this paper, we proposed new method of edge detection for active contours based on local adaptive threshold technique via variational energy minimization to stop the contour at desired object boundary. By using proposed method, we can control the evaluation of curve with moderate value of α for all images. As an application, our method has been used for tumor segmentation from magnetic resonance images and experimental results show desirable performances of our method.