Multivariate statistical model for 3D image segmentation with application to medical images

Abstract

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Figure 1

A general block diagram showing how the developed segmentation (shaded) fits into an overall system.

Figure 2

Values of alpha obtained from the sequence 1_24 (log scale).

Figure 3

Prior probability variation across iterations using “zero-crossing” detection.

Figure 4

Overlap coefficient versus iterations for different sequences.

Figure 5

Sample image segmentations (sequence 100_23).