Abstract
Abstract Computer-aided detection(CAD) system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and cost-sensitive support vector machine(C-SVM) classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO) nodules(part solid and nonsolid). This method consists of several steps. Firstly, segmentation of regions of interest(ROIs), including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentatio...
Highlights
Lung cancer is one of the most common malignant tumors
2) In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed in this paper, the image denoising and enhancing is realized by NADF method; candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function
An improved intelligent recognition method of lung nodule in HRCT combing rule-based and support vector machine (SVM) classifiers is proposed for detecting different types of lung nodules, including both solid nodules and ground-glass opacity(GGO) nodules(part solid and nonsolid)
Summary
Lung cancer is one of the most common malignant tumors. According to World Health Organization(WHO, 2004), Lung cancer, the most common cause of cancerrelated death in men and women, is responsible for 1.3 million deaths worldwide annually, as of 2004. An improved lung nodule intelligent recognition method combing rule-based and SVM classifiers is proposed for detecting multiple nodules, including solid nodules and ground-glass opacity(GGO) nodules(part solid and nonsolid). 2) In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed in this paper, the image denoising and enhancing is realized by NADF method; candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Different types of challenging nodules such as small nodules, low-contrast part-solid and nonsolid nodules are identified
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