Localised edge‐region‐based active contour for medical image segmentation

• Main Authors: Hua‐Xiang Liu, Jiang‐Xiong Fang, Zi‐Jian Zhang, Yong‐Cheng Lin
• Format: Article
• Language: English
• Published: Wiley 2021-05-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12126
• ISSN: 1751-9659; 1751-9667

Abstract Segmenting the region of interest (ROI) from medical images is a fundamental but challenging task due to the illumination change and imaging devices. Although many models based on the local region‐based active contour model (LR‐ACM) are proposed to deal with intensity inhomogeneity, it is still difficult for the global energy‐based ACM with local image information to accurately extract the ROI from medical images. To solve this problem, this study proposes a novel localised ACM by constructing the gradient information based on the probability scores from the fuzzy k‐nearest neighbour classifier. Different from the traditional LR‐ACMs, our model is based on local rather than global image statistics, where a probability score‐based edge detector is directly used for gradient information. The energy functional consists of localised region energy and an edge energy. By introducing a local characteristics function, the localised region energy with the probability‐score‐based edge information is formulated, which can make the evolution curve stop on the exact boundaries of ROI. The edge energy including the regularisation and the penalty terms is used to avoid the reinitialisation process and smooth the evolution curve during evolution. To maintain the signed distance property of the evolution curve, a novel potential function in the penalty term is designed, which can consistently control the diffusion direction of the evolution curve. Experiments on the medical images including the cardiac magnetic resonance imaging and the 3DIRCADb databases demonstrate that the proposed model is more robust and accurate to extract the ROI than the popular localised and region‐based ACMs. The code is available at: https://github.com/HuaxiangLiu/Localized_ERAC/.