Efficient 3D volume segmentation of MR images by a modified deterministic annealing approach

Zhanyu Ge; Sunanda Mitra

doi:10.1117/12.431025

3 July 2001 Efficient 3D volume segmentation of MR images by a modified deterministic annealing approach

Zhanyu Ge, Sunanda Mitra

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431025
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

This paper presents the results of applying the deterministic annealing (DA) algorithm to simulated magnetic resonance image segmentation. The applicability of this methodology for 3-D segmentation has been rigorously tested by using the simulated MRI volumes of normal brain at the Brain Web [8] for the 181 slices and whole volume of different modalities (T1, T2, and PD) without and with various levels of noise and intensity inhomogeneities. With proper thresholding of the clusters formed by the modified DA almost zero misclassification was achieved without the presence of noise. Even up to 7% addition of noise and 40% inhomogeneity, the average misclassification rates of the voxels belonging to white matter, gray matter, and cerebrospinal fluid were found to be less than 5% after median filtering. The accuracy, stability, global optimization and speed of the DA algorithm for 3-D MR image segmentation could provide a more rigorous tool for identification of diseased brain tissues from 3-D MR images than other existing 3-D segmentation techniques. Further inquiry into the DA algorithm shows that it is a Bayesian classifier with the assumption that the data to be classified follow a multivariate normal distribution. The characteristic of being a Bayesian classifier guarantees its achievement of global optimization.

Citation Download Citation

Zhanyu Ge and Sunanda Mitra "Efficient 3D volume segmentation of MR images by a modified deterministic annealing approach", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431025

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available