Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model

Abstract

Initial candidate segmentation is an important task in lung nodule detection. If an accurate segmentation is used, the false positives (FPs) can be decreased in subsequent stages and the desired region of each candidate can be distinguished. In this paper, a new hybrid method for nodule candidate segmentation and FPs reduction is proposed. First, the images are transferred to the neutrosophic domain. Then, three filters, named blob-like structure enhancement (BSE), line structure enhancement (LSE), and central adaptive medialness (CAM) filters, are used for filtering the output of the last step. Afterward, the outputs of BSE, LSE, and CAM filters are used for initial candidate detection and candidate segmentation, respectively. Also, line tracking method is proposed for extending the candidate voxels, and then, it is used for candidate segmentation. After feature extraction, the sparse coding is used for learning feature vector. In the last step, the generalized linear regression model (GLRM) is used for classification. The output of classifier for sensitivity and FP/scan is 98.32% and 2.8, respectively. AUC values of different link functions in GLRM, before and after feature learning, were also calculated, and the best value of AUC was obtained with probit link function after using the sparse coding method. The experimental results demonstrate the power of the proposed algorithm in nodules detection and false positive reduction.

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