SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix
SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix
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基于 SVM 的 GLCM 矩阵奇异值分解表征肝硬化
DOI：
10.1504/ijaisc.2013.053407
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
N. Khandelwal
中科院分区：
文献类型：
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作者：
J. Virmani;Vinod Kumar;N. Kalra;N. Khandelwal
肝硬化的早期诊断是必要的，因为肝硬化是一种不可逆的疾病，最常被视为发展为肝细胞癌的前兆。早期诊断有助于放射科医生通过适当的治疗方案安排更好的疾病管理。在目前的工作中，从GLCM平均矩阵、GLCM范围矩阵和GLCM矩阵的奇异值分解中得到的特征已经与SVM分类器一起用于设计一个有效的计算机辅助诊断系统来表征正常和肝硬化的肝脏。本研究从31张临床获得的b型肝脏超声图像中提取了120个感兴趣的roi区域。结果表明，对GLCM矩阵进行奇异值分解得到的前四个奇异值，准确率和灵敏度最高，分别为98.33%和100%。所提出的计算机辅助诊断系统所获得的令人满意的结果表明，它有助于放射科医生诊断肝硬化。
Early diagnosis of liver cirrhosis is essential as cirrhosis is an irreversible disease most often seen as precursor to development of hepatocellular carcinoma. Early diagnosis helps radiologist in better disease management by adequate scheduling of treatment options. In the present work, features derived from GLCM mean matrix, GLCM range matrix and singular value decomposition of GLCM matrix have been used along with SVM classifier for designing an efficient computer-aided diagnostic system to characterise normal and cirrhotic liver. The study has been carried out on 120 regions of interest ROIs extracted from 31 clinically acquired B-mode liver ultrasound images. It is observed that the first four singular values obtained by singular value decomposition of GLCM matrix result in highest accuracy and sensitivity of 98.33% and 100%, respectively. The promising results obtained by the proposed computer-aided diagnostic system indicate its usefulness to assist radiologists in diagnosis of liver cirrhosis.