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International Journal of Biomedical Imaging
Volume 2015, Article ID 267807, 7 pages
Research Article

Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

Computer & Systems Department, Electronics Research Institute, Cairo 12611, Egypt

Received 23 May 2015; Revised 10 September 2015; Accepted 15 September 2015

Academic Editor: Tiange Zhuang

Copyright © 2015 Nourhan Zayed and Heba A. Elnemr. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.