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Proceedings Paper

A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules
Author(s): Fangfang Han; Huafeng Wang; Bowen Song; Guopeng Zhang; Hongbing Lu; William Moore; Hong Zhao; Zhengrong Liang
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Paper Abstract

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

Paper Details

Date Published: 28 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702Z (28 February 2013); doi: 10.1117/12.2007252
Show Author Affiliations
Fangfang Han, Stony Brook Univ., SUNY (United States)
Northeastern Univ. (China)
Huafeng Wang, Stony Brook Univ., SUNY (United States)
Bowen Song, Stony Brook Univ., SUNY (United States)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Hongbing Lu, Fourth Military Medical Univ. (China)
William Moore, Stony Brook Univ., SUNY (United States)
Hong Zhao, Northeastern Univ. (China)
Zhengrong Liang, Stony Brook Univ., SUNY (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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