Pulmonary nodule detection in CT images based on shape constraint CV model
- 19 February 2015
- journal article
- radiation imaging-physics
- Published by Wiley in Medical Physics
- Vol. 42 (3), 1241-1254
- https://doi.org/10.1118/1.4907961
Abstract
Purpose: Accurate detection of pulmonary nodules remains a technical challenge in computer‐aided diagnosis systems because some nodules may adhere to the blood vessels or the lung wall, which have low contrast compared to the surrounding tissues. In this paper, the analysis of typical shape features of candidate nodules based on a shape constraint Chan–Vese (CV) model combined with calculation of the number of blood branches adhered to nodule candidates is proposed to reduce false positive (FP) nodules from candidate nodules. Methods: The proposed scheme consists of three major stages: (1) Segmentation of lung parenchyma from computed tomography images. (2) Extraction of candidate nodules. (3) Reduction of FP nodules. A gray level enhancement combined with a spherical shape enhancement filter is introduced to extract the candidate nodules and their sphere‐like contour regions. FPs are removed by analysis of the typical shape features of nodule candidates based on the CV model using spherical constraint and by investigating the number of blood branches adhered to the candidate nodules. The constrained shapes of CV model are automatically achieved from the extracted candidate nodules. Results: The detection performance was evaluated on 127 nodules of 103 cases including three types of challenging nodules, which are juxta‐pleural nodules, juxta‐vascular nodules, and ground glass opacity nodules. The free‐receiver operating characteristic (FROC) curve shows that the proposed method is able to detect 88% of all the nodules in the data set with 4 FPs per case. Conclusions: Evaluation shows that the authors’ method is feasible and effective for detection of three types of nodules in this study.Keywords
Funding Information
- National Natural Science Foundation of China (61271318)
- National Natural Science Foundation of China (61375075)
- National Natural Science Foundation of China (NSFC) (61190120-61190124)
- Natural Science Foundation of Hebei Province (Hebei Provincial Natural Science Foundation) (F2012201020)
- National Natural Science Foundation of China (61271318)
- National Natural Science Foundation of China (61375075)
- National Natural Science Foundation of China (61190120‐61190124)
- Natural Science Foundation of Hebei Province (F2012201020)
- National Natural Science Foundation of China (61271318, 61375075, 61190120-61190124)
- Natural Science Foundation of Hebei Province (F2012201020)
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