Fuzzy-Snake Segmentation of Anatomical Structures Applied to CT Images

Abstract

This paper presents a generic strategy to facilitate the segmentation of anatomical structures in medical images. The segmentation is performed using an adapted PDM by fuzzy c-means classification, which also uses the fuzzy decision to evolve PDM into the final contour. Furthermore, the fuzzy reasoning exploits a priori statistical information from several knowledge sources based on histogram analysis and the intensity values of the structures under consideration. The fuzzy reasoning is also applied and compared to a geometrical active contour model (or level set). The method has been developed to assist clinicians and radiologists in conformai RTP. Experimental results and their quantitative validation to assess the accuracy and efficiency are given segmenting the bladder on CT images. To assess precision, results are also presented in CT images with added Gaussian noise. The fuzzy-snake is free of parameter and it is able to properly segment the structures by using the same initial spline curve for a whole study image-patient set. © Springer-Verlag 2004.