Nosologic imaging of the brain: segmentation and classification using MRI and MRSI

被引:41
作者
Luts, Jan [1 ]
Laudadio, Teresa [1 ,2 ]
Idema, Albert J. [3 ]
Simonetti, Arjan W. [4 ]
Heerschap, Arend [5 ]
Vandermeulen, Dirk [6 ]
Suykens, Johan A. K. [1 ]
Van Huffel, Sabine [1 ]
机构
[1] Katholieke Univ Leuven, Dept Elect Engn ESAT, Res Div SCD, B-3001 Louvain, Belgium
[2] CNR, Ist Applicaz Calcolo, Sez Bari, I-70126 Bari, Italy
[3] Univ Nijmegen, Dept Neurosurg, Univ Med Ctr, NL-6500 HB Nijmegen, Netherlands
[4] Philips Med Syst, NL-5680 DA Best, Netherlands
[5] Univ Nijmegen, Dept Radiol, Univ Med Ctr, NL-6500 HB Nijmegen, Netherlands
[6] Katholieke Univ Leuven, Dept Elect Engn ESAT, Res Div PSI, B-3001 Louvain, Belgium
关键词
brain tumor; nosologic image; magnetic resonance imaging (MRI); magnetic resonance spectroscopic imaging (MRSI); classification; segmentation; class probabilities; KERNEL LOGISTIC-REGRESSION; TUMOR SEGMENTATION; SPECTROSCOPY; IMAGES; GLIOMAS; MODEL; QUANTIFICATION; FRAMEWORK; SYSTEM;
D O I
10.1002/nbm.1347
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject-specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high-resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making. Copyright 2008 John Wiley & Sons, Ltd.
引用
收藏
页码:374 / 390
页数:17
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