A novel multiphase active contour model for inhomogeneous image segmentation

• Published in: Multimedia tools and applications Vol. 72; no. 3; pp. 2321 - 2337
• Main Authors: Gao, Shangbing, Yang, Jian, Yan, Yunyang
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2014; Springer; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial intelligence; Bias; Boundaries; Computer Communication Networks; Computer Science; Computer science; control theory; systems; Data Structures and Information Theory; Evolution; Exact sciences and technology; Image processing systems; Image retrieval; Image segmentation; Methods; Multimedia; Multimedia communications; Multimedia Information Systems; Multiphase; Noise; Pattern recognition. Digital image processing. Computational geometry; Segments; Shape; Special Purpose and Application-Based Systems; Studies; United States > US; Active contour model; Intensity inhomogeneity; Natural image; Local binary fitting (LBF) model; Chan-Vese (CV) model; Initialization; Computer vision; Image processing; Bias; Magnetic resonance; Local theory; Contour line; Nuclear magnetic resonance imaging; Modeling; Image segmentation; Natural scenes; Edge detection; Active contour
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-013-1553-2

The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images segmentation. The complex images usually contain an arbitrary number of objects. This paper presents a new multiphase active contour model method for simultaneous regions classification of MR images and natural images without bias field correction. In this model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the multiple regions. This model not only extracts multiple objects simultaneously, but also provides smooth and accurate boundaries of the objects. The results for experiments on several synthetic and real images demonstrate the effectiveness and accuracy of our model.