Active Contours based on An Anisotropic Diffusion

Image Segmentation is one of the pivotal procedure in the field of imaging and its objective is to catch required boundaries inside an image. In this paper, we propose a novel active contour method based on anisotropic diffusion. Global region-based active contour methods rely on global intensity information across the regions. However, these methods fail to produce desired segmentation results when an image has some background variations or noise. In this regard, we adapt Perona and Malik smoothing technique as enhancement step. This technique provides interregional smoothing, sharpens the boundaries and blurs the background of an image. Our main role is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. Minimizing an energy function using partial differential framework produce results with semantically meaningful boundaries instead of capturing impassive regions. Finally, we use Gaussian kernel to eliminate problem of reinitialization in level set function. We use images taken from different modalities to validate the outcome of the proposed method. In the result section, we have evaluated that, the proposed method achieves good results qualitatively and quantitatively with high accuracy compared to other state-of-the-art models.