A Novel Level Set Model Originated from Fuzzy Connectedness Guided Initial Contours

Level set models are widely used in the image segmentation field. However, the sensitivity of the initial contours and the manual adjustment of the controlling parameters have limited the segmentation performance. To effectively solve this problem, a novel level set model utilizing both intensity and spatial information is proposed in this paper. Firstly, the fuzzy connectedness (FC) algorithm is applied to obtain the appropriate initial contours, and as a result the complexity and computation cost of building initial contours is reduced. Secondly, based on the morphological characteristics of the initial contours and the parameters of fuzzy connectedness, several equations are proposed to automatically estimate the controlling parameters of the level set evolution and avoid human intervention. Finally, the region-scalable fitting (RSF) model is adopted to evolve and obtain the final robust segmentation results. The efficiency and accuracy of the model proposed in this paper is verified by comparing the three quantitative indexes of time, Dice similarity coefficient (DSC) and peak signal to noise ratio (PSNR) with four different initialized level set models.