LV Segmentation Using Stochastic Resonance and Evolutionary Cellular Automata

Abstract

High-level noise and low contrast characteristics in medical images continue to present major bottlenecks in their segmentation despite increased imaging modalities. This paper presents a semi-automatic algorithm that utilizes the noise for enhancing the contrast of low contrast input magnetic resonance images followed by a new graph cut method to reconstruct the surface of left ventricle. The main contribution in this work is a new formulation for preventing the conventional cellular automata method to leak into surrounding regions of similar intensity. Instead of segmenting each slice of a subject sequence individually, we empirically select a few slices, segment them, and reconstruct the left ventricular surface. During the course of surface reconstruction, we use level sets to segment the rest of the slices automatically. We have throughly evaluated the method on both York and MICCAI Grand Challenge workshop databases. The average Dice coefficient (in %) is found to be 92.4 ± 1.3 (value indicates the mean and standard deviation) whereas false positive ratio, false negative ratio, and specificity are found to be 0.019, 7.62 × 10-3, and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.94 mm. The encouraging quantitative and qualitative results reflect the potential of the proposed method.