Segmentation algorithm of intervertebral disc magnetic resonance images based on two-dimensional automatic active shape model

Abstract

In response to the issue that the intervertebral disk manual modeling was time-consuming and subjective, and the existing segmentation method was not accurate enough, a new method named two-diememsional Automatic Active Shape Model( 2D-AASM) was proposed. It included three parts: automatic statistical shape modeling of intervertebral disk based on minimum description length, 2D local gradient modeling and segmentation. Adopting the manual segmentation results of 25sets of spinal MR images as the training set, the study used minimum description length method to determine the point correspondence, built statistical shape model and 2D local gradient model for intervertebral disk T4-5. The generated shape model had lower variance and the objective function value than the manual and arc length parameter method. After the model was built, three sets of Magnetic Resonance Image( MRI) images were used to test the proposed method. Compared with the traditional ASM and 1D-ASM, the segmentation result of the proposed method had a higher Dice coefficient and lower oversegmentation and under-segmentation rate. The experiment results indicate that the proposed method generates a better model and more accurate segmentation result.