A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

Abstract

Abstract-Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.Index Terms-Bias correction, image segmentation, intensity inhomogeneity, level set, MRI.

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

“…In the two-region case, the image domain Ω can be segmented into two disjoint regions Ω 1 and Ω 2 . In Equation (7), the regions Ω 1 and Ω 2 are represented with their membership functions defined by M 1 (φ(x)) and M 2 (φ(x)) [35], respectively. Different from Equation (7), we first use η i (x), which denotes the membership function of the region Ω i .…”

Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation

“…Among the images segmentation algorithms with intensity in-homogeneities correction on GPU architecture, authors in [16] proposed an extended mask-based version of the level set method with bias field, recently presented by Li et al [17]. They develop CUDA implementations for the original full domain and the extended mask-based versions, and compare the methods in terms of speed, efficiency, and performance.…”

An active contour model for medical image segmentation with application to brain CT image

“…[4][5][6][7][8][9][10][11] However, these methods are not suitable for the segmentation of brain CT images, because brain CT images generally have lower tissue contrast and higher noise level than MRI. In fact, only limited literature about the segmentation of brain CT images has been published.…”

“…5,[18][19][20][21][22][23][24][25][26][27] Existing active contours models can be classified into two categories: edge-based models [18][19][20][21] and region-based models. [22][23][24][25][26][27] The geodesic active contour model 21 (GAC) is a typical edgebased model.…”