S. P. Kamalapriya, S . PathurNisha, Nehru Institute of Technology, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.01.05
International Journal of Advanced Information and Communication Technology
Received On : February 10, 2014
Revised On : March 20, 2014
Accepted On : April 15, 2014
Published On : May 05, 2014
Volume 01, Issue 01
Pages : 031-035
Abstract
The growing size and number of the medical images necessitated the use of computers to facilitate processing and analysis. In medical world diagnostic Imaging is an invaluable and important tool for earlydetection of diseases. An enhanced feature descriptor that categories lung tissues in High Resolution Computed Tomography (HRCT) images used for Computer Aided Diagnosis is proposed in this paper. The images are divided into multiple Image Patches called AROI (Annotated Region of Interest).Image features like Texture, intensity and gradient are considered for feature extraction and classification. Labeling is done using a new patch-adaptive sparse approximation method. The proposed method is evaluated on apublicly available Interstitial Lung Disease (ILD) database to show the performance improvement.
Keywords
Gradient, Texture, feature descriptor, classifiers.
Cite this article
S.P.Kamalapriya, S.PathurNisha, “Enhanced Image Patch Approximation for Lung Tissue Classification Using Feature Extraction,” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 31-35, May. 2014.
Copyright
© 2014 S.P.Kamalapriya, S.PathurNisha. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.