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

Abstract

Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods:The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images.

Segmentation of skin lesion using Cohen–Daubechies–Feauveau biorthogonal wavelet

“…However, it also has the few drawbacks such as they are sensitive to local minima state, minute feature are often ignored and their accuracy depend on the convergence policy (Qian et al. 2013 ). An active contour or a simple elastic snake can be represented by the energy function defined by n points where like Energy function of snake is sum of its external and internal energy.…”

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“…Since, in these procedures, the operator will initialize the segmentation task by indicating the suitable location of the tumor in the brain MRI. Procedures like, Active-Contour (AC) [23,[34][35][36], Seed-Region-Growing (SRG) [37,38], Chan-Vese (CV) [8,39], and Distance-Regularised-Level-Set (DRLS) [40,41], falls in this category, which does not worry about the skull section. Hence the choice of skull stripping depends mainly on the CDT implemented to evaluate the brain abnormality.…”

“…It needs the initiation of a seed at a point or a pixel on the ROI. When a seed point is identified, the section will cultivate by connecting possible similar neighboring pixels available in ROI [34]. SRG is one of the widely adopted semi-automated image segmentation scheme [8].…”

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“…The main advantage of the MSER algorithm is that there is no need to specify an initial contour, which is necessary and often drawn manually in other algorithms. For example, brain tissue segmentation approaches based on Active Contour Models [28,29,30,31,32] require an initial contour. Furthermore, the region stability of MSER is constrained by local information obtained in the neighbourhood and can accommodate large intra-image variations [25].…”