A new active contour model driven by pre-fitting bias field estimation and clustering technique for image segmentation. (September 2021)

• Record Type: Journal Article
• Authors: Weng, Guirong; Dong, Bin
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Active contour model -- Bias field -- K-means++ -- Intensity inhomogeneity -- Image segmentation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104299
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Due to uneven illumination or limitations of imaging devices, intensity inhomogeneities are more or less present in images obtained by different imaging modes. This ubiquitous intensity inhomogeneity makes image segmentation more difficult. This paper proposes a new bias field model (KPBFE) based on pre-fitting bias field estimation to deal with intensity inhomogeneity in the image segmentation. A new function for computing bias field b is proposed with K-means++ clustering algorithm. The computation method of clustering center points takes into account the average value of the grayscale within the contour of the bias field estimation and outside the contour. Meanwhile, we use a variational level set function with arctan function and a new adaptive function τ to limit the magnitude of the data driver term. Since the computation of bias field estimation is completed before the iteration and there is no convolution operation in the process, the computing speed of the proposed model is greatly increased. Experiments results show that our model can effectively segment the images with intensity inhomogeneity. Compared with some classical models, our method also has faster computation speed, higher segmentation accuracy and better initial robustness. Highlights: A new function for computing bias field is proposed. The computation of the bias field is completed before the iteration and there is no more time-consuming convolutions in iterations. The computing of clustering center points takes into account the average value of the grayscale within and without the contour of the bias field. The variational level set function is optimized with an adaptive function to limit the magnitude of the data driven term. The proposed model shows better segmentation accuracy and robustness.