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http://dx.doi.org/10.21288/resko.2016.10.4.259

Study of Joint Histogram Based Statistical Features for Early Detection of Lung Disease  

Won, Chul-ho (경일대학교 의용공학과)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.10, no.4, 2016 , pp. 259-265 More about this Journal
Abstract
In this paper, new method was proposed to classify lung tissues such as Broncho vascular, Emphysema, Ground Glass Reticular, Ground Glass, Honeycomb, Normal for early lung disease detection. 459 Statistical features was extraced from joint histogram matrix based on multi resolution analysis, volumetric LBP, and CT intensity, then dominant features was selected by using adaboost learning. Accuracy of proposed features and 3D AMFM was 90.1% and 85.3%, respectively. Proposed joint histogram based features shows better classification result than 3D AMFM in terms of accuracy, sensitivity, and specificity.
Keywords
Lung Tissue; Adaboost; Computer Aided Diagnosis; Volumetric LBP;
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