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Proceedings Paper

Automatic segmentation method of striatum regions in quantitative susceptibility mapping images
Author(s): Saki Murakawa; Yoshikazu Uchiyama; Toshinori Hirai
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Paper Abstract

Abnormal accumulation of brain iron has been detected in various neurodegenerative diseases. Quantitative susceptibility mapping (QSM) is a novel contrast mechanism in magnetic resonance (MR) imaging and enables the quantitative analysis of local tissue susceptibility property. Therefore, automatic segmentation tools of brain regions on QSM images would be helpful for radiologists’ quantitative analysis in various neurodegenerative diseases. The purpose of this study was to develop an automatic segmentation and classification method of striatum regions on QSM images. Our image database consisted of 22 QSM images obtained from healthy volunteers. These images were acquired on a 3.0 T MR scanner. The voxel size was 0.9×0.9×2 mm. The matrix size of each slice image was 256×256 pixels. In our computerized method, a template mating technique was first used for the detection of a slice image containing striatum regions. An image registration technique was subsequently employed for the classification of striatum regions in consideration of the anatomical knowledge. After the image registration, the voxels in the target image which correspond with striatum regions in the reference image were classified into three striatum regions, i.e., head of the caudate nucleus, putamen, and globus pallidus. The experimental results indicated that 100% (21/21) of the slice images containing striatum regions were detected accurately. The subjective evaluation of the classification results indicated that 20 (95.2%) of 21 showed good or adequate quality. Our computerized method would be useful for the quantitative analysis of Parkinson diseases in QSM images.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142G (20 March 2015); doi: 10.1117/12.2081119
Show Author Affiliations
Saki Murakawa, Kumamoto Univ. (Japan)
Yoshikazu Uchiyama, Kumamoto Univ. (Japan)
Toshinori Hirai, Kumamoto Univ. (Japan)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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