Dictionary learning-based CT detection of pulmonary nodules

Panpan Wu; Kewen Xia; Yanbo Zhang; Xiaohua Qian; Ge Wang; Hengyong Yu

doi:10.1117/12.2236780

3 October 2016 Dictionary learning-based CT detection of pulmonary nodules

Panpan Wu, Kewen Xia, Yanbo Zhang, Xiaohua Qian, Ge Wang, Hengyong Yu

Proceedings Volume 9967, Developments in X-Ray Tomography X; 99671S (2016) https://doi.org/10.1117/12.2236780
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

Citation Download Citation

Panpan Wu, Kewen Xia, Yanbo Zhang, Xiaohua Qian, Ge Wang, and Hengyong Yu "Dictionary learning-based CT detection of pulmonary nodules", Proc. SPIE 9967, Developments in X-Ray Tomography X, 99671S (3 October 2016); https://doi.org/10.1117/12.2236780

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KEYWORDS

Associative arrays

Image segmentation

Feature extraction

Chemical species

Actinium

Technetium

Lung

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