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Proceedings Paper

Risk prediction of small pulmonary nodules based on novel CT image texture markers
Author(s): Fangfang Han; Bowen Song; He Ma; Wei Qian; Zhengrong Liang
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Paper Abstract

Among the detected small nodules sized from 3 to 30mm in CT images, a significant portion is undetermined in terms of malignancy which needs biopsy or other follow-up means, resulting in excessive risk and cost. Therefore, predicting the malignancy of the nodules becomes a clinically desirable task. Based on the previous study of texture features extracted from gray-tone spatial-dependence matrices, this study aims to find more efficient texture features or image texture markers in discriminating the nodule malignancy. Two new image texture markers (median and variance) are proposed to classify the small nodules into different malignant levels, thus the risk prediction could be performed through image analysis. These two new image texture markers can minimize the effect of outliers in the feature series, thus can reduce the noise influence to the feature classification. Total 1,353 nodule samples selected from the Lung Image Database Consortium were used to evaluate the efficiency of the proposed new features. All the classification results are shown in the ROC curves and tabulated by the AUC values. The classification outcomes from (1) the most likely and likely benign nodules vs. the most likely and likely malignant nodules, (2) the most likely vs. likely benign nodules, and (3) the most likely vs. likely malignant nodules, are 0.9125±0.0096, 0.9239±0.0147, and 0.8888±0.0197, respectively, in terms of the largest AUC values. From the experimental outcomes on different malignant levels, the two new image texture markers from nodule volumetric CT image data have shown encouraging performance for the risk prediction.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343Q (3 March 2017); doi: 10.1117/12.2253486
Show Author Affiliations
Fangfang Han, Northeastern Univ. (China)
Bowen Song, Ocean Univ. of China (China)
He Ma, Northeastern Univ. (China)
Wei Qian, Univ. of Texas at El Paso (United States)
Northeastern Univ. (China)
Zhengrong Liang, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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