Knowledge-driven segmentation of the central sulcus from human brain MR images

Author

Zuo, Wei ; Hu, Qingmao ; Aziz, Aamer ; Loe, Kiafock ; Nowinski, Wieslaw L.

Author_Institution

Biomed. Imaging Group, Nat. Univ. of Singapore, Singapore

Volume

4

fYear

2004

fDate

24-27 Oct. 2004

Firstpage

2443

Abstract

This paper presents a knowledge-driven algorithm to identify and segment the central sulcus (CS) from human brain MR images. The dataset is reformatted along the anterior and posterior commissures (AC-PC) plane first. Then, the 3D region within the two coronal planes passing through the AC and PC is defined as the region of interest (ROl) to search for all the sulci within it. The CS is the sulcus with the largest volume within the ROI. Together with the sulci, grey matter (GM) is included for the region growing in order to deal with the partial volume effect. The GM is removed through skeletonization. Experimental results are given.

Keywords

biomedical MRI; brain; image segmentation; medical image processing; neurophysiology; AC-PC plane first; CS; GM; ROl; anterior-posterior commissure; central sulcus; coronal plane; dataset; grey matter; human brain MR image; knowledge-driven segmentation; partial volume effect; region of interest; skeletonization; Bioinformatics; Biomedical imaging; Brain; Humans; Image segmentation; Joining processes; Lesions; Magnetic resonance imaging; Skeleton; Skull;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2004. ICIP '04. 2004 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-8554-3

Type

conf

DOI

10.1109/ICIP.2004.1421595

Filename

1421595