Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity

• Published in: PloS one Vol. 12; no. 4; p. e0174813
• Main Authors: Akram, Farhan, Garcia, Miguel Angel, Puig, Domenec
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.04.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Approximation; Artificial Intelligence; Bias; Biology and Life Sciences; Biomedical engineering; Boundaries; Brain; Brain - diagnostic imaging; Calculi; Cerebrospinal fluid; Classification; Computational neuroscience; Computer applications; Computer engineering; Computer programs; Contours; Cortex; Data processing; Decision making; Differential equations; Ecology; Energy; Engineering; Flora; Functionals; Homogeneity; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Image segmentation; Information processing; Inhomogeneity; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Mathematical problems; Medical imaging; Medical personnel; Medicine and Health Sciences; Methods; Models, Theoretical; Neuroimaging; Neuroimaging - methods; Noise; Normal Distribution; Partial differential equations; Pattern recognition; Physical Sciences; Research and Analysis Methods; Signal processing; Skull; Software; Statistical analysis; Vision
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0174813

This paper presents a region-based active contour method for the segmentation of intensity inhomogeneous images using an energy functional based on local and global fitted images. A square image fitted model is defined by using both local and global fitted differences. Moreover, local and global signed pressure force functions are introduced in the solution of the energy functional to stabilize the gradient descent flow. In the final gradient descent solution, the local fitted term helps extract regions with intensity inhomogeneity, whereas the global fitted term targets homogeneous regions. A Gaussian kernel is applied to regularize the contour at each step, which not only smoothes it but also avoids the computationally expensive re-initialization. Intensity inhomogeneous images contain undesired smooth intensity variations (bias field) that alter the results of intensity-based segmentation methods. The bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. In this paper, a two-phase model is first derived and then extended to a four-phase model to segment brain magnetic resonance (MR) images into the desired regions of interest. Experimental results with both synthetic and real brain MR images are used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation technique in practical terms.