Segmentation of interest region in medical volume images using geometric deformable model

Abstract

In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccard's measure score (72.52%–94.17%) and lower Hausdorff distance (1.2654mm–3.1527mm) than the other methods such as mean speed (67.67%–93.36% and 1.3361mm–3.4463mm), mean-variance speed (63.44%–94.72% and 1.3361mm–3.4616mm), and edge-based speed (0.76%–42.44% and 3.8010mm–6.5389mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.