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Proceedings Paper

Texture classification of lung computed tomography images
Author(s): Hang See Pheng; Siti Mariyam Shamsuddin
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Paper Abstract

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Paper Details

Date Published: 20 March 2013
PDF: 5 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87683Z (20 March 2013); doi: 10.1117/12.2011108
Show Author Affiliations
Hang See Pheng, Univ. Teknologi Malaysia (Malaysia)
Siti Mariyam Shamsuddin, Univ. Teknologi Malaysia (Malaysia)


Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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