Ms. Saki Murakawa Profile

Saki Murakawa

at Kumamoto Univ

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Proceedings Article | 20 March 2015 Paper

Automatic segmentation method of striatum regions in quantitative susceptibility mapping images

Saki Murakawa, Yoshikazu Uchiyama, Toshinori Hirai

Proceedings Volume 9414, 94142G (2015) https://doi.org/10.1117/12.2081119

KEYWORDS: Image segmentation, Magnetic resonance imaging, Quantitative analysis, Image classification, Image registration, Computer aided diagnosis and therapy, Head, 3D image processing, Brain mapping, Brain

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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.

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