A 3-D Active Shape Model Driven by Fuzzy Inference: Application to Cardiac CT and MR

Abstract

Manual quantitative analysis of cardiac left ventricular function using Multislice CT and MR is arduous because of the large data volume. In this paper, we present a 3-D active shape model (ASM) for semiautomatic segmentation of cardiac CT and MR volumes, without the requirement of retraining the underlying statistical shape model. A fuzzy c-means based fuzzy inference system was incorporated into the model. Thus, relative gray-level differences instead of absolute gray values were used for classification of 3-D regions of interest (ROIs), removing the necessity of training different models for different modalities/acquisition protocols. The 3-D ASM was evaluated using 25 CT and 15 MR datasets. Automatically generated contours were compared to expert contours in 100 locations. For CT, 82.4% of epicardial contours and 74.1% of endocardial contours had a maximum error of 5 mm along 95% of the contour arc length. For MR, those numbers were 93.2% (epicardium) and 91.4% (endocardium). Volume regression analysis revealed good linear correlations between manual and semiautomatic volumes, r(2) >/= 0.98. This study shows that the fuzzy inference 3-D ASM is a robust promising instrument for semiautomatic cardiac left ventricle segmentation. Without retraining its statistical shape component, it is applicable to routinely acquired CT and MR studies.