A General Approach to Segmentation in CT Grayscale Images using Variable Neighborhood Search

Medical image segmentation is essential for several tasks including pre-treatment planning and tumor monitoring. Computed tomography (CT) is the most useful imaging modality for abdominal organs and tumors, with benefits of high imaging resolution and few motion artifacts. Unfortunately, CT images contain only limited information of intensity and gradient, which makes accurate segmentation a challenge. In this paper, we propose a 2D segmentation method that applies the concept of variable neighborhood search (VNS) by iteratively alternating search through intensity and gradient spaces. By alternating between the two search spaces, the technique can escape local minima that occur when segmenting in a single search space. The main techniques used in the proposed framework are graph-cut with probability density function (GCPDF) and graph-cut based active contour (GCBAC). The presented method is quantitatively evaluated on a public clinical dataset, which includes various sizes of liver tumor, kidney and spleen. The segmentation performance is evaluated using dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and volume difference (VD). The presented method achieves the outstanding segmentation performance with a DSC of 84.48±5.84%, 76.93±8.24%, 91.70±2.68% and 89.27±5.21%, for large liver tumor, small liver tumor, kidney and spleen, respectively.