Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in T2^＊-w magnetic resonance imaging

• Published in: 中国物理B：英文版 no. 12; pp. 610 - 615
• Main Author: 余绍德 伍世宾 王浩宇 魏新华 陈鑫 潘万龙 Hu Jiani 谢耀钦
• Format: Journal Article
• Language: English
• Published: 01.12.2015
• Subjects: 交叉验证; 供应链管理; 强度分析; 相似度; 相异分析; 磁共振成像; 线性拟合; 组织分类
• ISSN: 1674-1056; 2058-3834
• DOI: 10.1088/1674-1056/24/12/128711

Similarity coefficient mapping（SCM） aims to improve the morphological evaluation of T＊2weighted magnetic resonance imaging（T＊2-w MRI）. However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM？ The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multiecho T＊2-w MRI. Generated maps were investigated from signal-to-noise ratio（SNR） and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.