Kidney segmentation in computed tomography sequences based on energy minimization

With the continuous development of medical imaging technology, medical image processing has played an increasingly prominent role in computer-aided diagnosis and disease management. Kidney segmentation in abdominal computed tomography (CT) sequences is a key step. In this paper, combining with the contextual property of renal tissues, a new energy minimization model based on active contour and graph cuts is proposed for kidney extraction in CT sequence. According to the relationship between the shape difference in adjacent slices and corresponding layer thickness, the optimal search range of the contour evolution is calculated for graph cut optimization. The energy function, combining the geodesic active contour with Chan-Vese model, takes into account the boundary and regional information. Then, graph cut methods are used to optimize the discrete energy function and drive the active contour towards object boundaries. Thirty abdominal CT sequences are used to evaluate the accuracy and effectiveness of the proposed algorithm. The experimental results reveal that this approach can extract renal tissues in CT sequences effectively and the average Dice coefficient reaches about 93.7%.