Segmentation of 3D object in volume dataset using active deformable model

The level set approach can be used as powerful tool for volume segmentation of a region-of-interest (ROI), to achieve an accurate estimation of tumor or soft tissue in medical images. A major challenge of such algorithms is required to set the equation parameters, especially in the speed function. In this paper, we introduce a geometric active surface scheme that uses level set approach for tumor segmentation in volume datasets by the surface evolution framework based on the geometric variation principle. In this scheme, the level set speed function is designed using hybrid information of geodesic active region and geodesic active contour. Our method handles topological changes of the deformable surface using geometric integral measures and the level set theory. These integral measures contain the robust alignment term, the active region term and the minimal surface term. The proposed algorithm is tested on medical images of the head for tumor segmentation and its performance is evaluated visually and quantitatively. The experimental results confirm the effectiveness of the proposed method and its superior performance when compared with traditional approaches.