Intelligent automated brain image segmentation. (27th August 2009)

• Record Type: Journal Article
• Authors: Seixas, F.L.; Conci, A.; Muchaluat-Saade, D.C.; De Souza, A.S.
• Is Part Of: International journal of innovative computing and applications. Volume 2:Number 1(2009)
• Page Start: 23
• Page End: 33
• Publication Date: 2009-08-27
• Subjects: image analysis -- brain image segmentation -- Alzheimers disease -- hippocampus -- aging -- dementia -- medical imaging -- computer-aided diagnosis -- magnetic resonance images -- voxel-based morphometry; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

The availability of modern computational techniques and advanced medical imaging protocols has increased the development of computer-aided diagnosis systems. This paper presents a fully automated brain structures segmentation algorithm for magnetic resonance (MR) images. Automated mechanisms reduce the excessive time consumed on manual segmentation and standardise the volumetric acquisition method. The proposed computational image segmentation method is based on a voxel-wise morphometry method, named voxel-based morphometry (VBM). The brain structure of interest of this paper is the hippocampus, a medial temporal lobe structure, precociously affected in Alzheimer's disease (AD), which represents the most common cause of dementia worldwide. We evaluated 371 subjects from OASIS database, including normal controls and probable Alzheimer's patients, splitting them in different age groups. Segmentation results demonstrated that grey matter and hippocampus volumes decrease in both groups proportionally to aging and it is more evident in AD subjects.