A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications
A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications作者机构：Department of Radiological Technology Faculty of Fukuoka Medical Technology Teikyo University Fukuoka Ja-pan Department of Radiology and Nuclear Medicine Research Institute for Brain and Vessels-Akita Akita Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan Department of Electrical and Computer Engineering National Institute of Technology Gifu College Gifu Japan Department of Biomedical Engineering Hungkuang University Taiwan
出 版 物：《Journal of Biomedical Science and Engineering》 (生物医学工程（英文）)
年 卷 期：2016年第9卷第6期
页 面：315-322页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Information Entropy Image and Texture Feature Computer-Aided Diagnosis Support Vector Machine
摘 要：Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications.
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