Pulmonary nodule detection in CT images based on shape constraint CV model

Medical Physics
Bing WangLixu Gu

Abstract

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. 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. The detection performance was evaluated on 127 nodules of 103 c...Continue Reading

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Citations

May 12, 2017·Medical Physics·Jingchen MaJun Zhao
Jun 28, 2019·Microscopy Research and Technique·Tanzila Saba
Jun 7, 2017·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·Hongbo ZhuXiangyun Ye
May 12, 2017·International Journal of Computer Assisted Radiology and Surgery·Dong ChenLixu Gu

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