Pathological liver segmentation using stochastic resonance and cellular automata

• We present a new method to segment low contrast liver CT images with high noise level.
• We utilize the noise constructively to enhance the input image contrast.
• The segmentation method is based on cellular automata.
• Level sets are used to generate segmentation of intermediate slices.
• The results show good segmentation accuracy when compared with ground truth images.

Liver segmentation continues to remain a major challenge, largely due to its intensity complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography data. In this paper, we present an approach to reconstructing the liver surface in low contrast computed tomography. The main contributions are: (1) a stochastic resonance based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulted by cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and MICCAI Grand Challenge workshop. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.