An active contour model for medical image segmentation with application to brain CT image. Issue 2 (29th January 2013)

• Record Type: Journal Article
• Authors: Qian, Xiaohua; Wang, Jiahui; Guo, Shuxu; Li, Qiang
• Is Part Of: Medical physics. Volume 40:Issue 2(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-01-29
• Subjects: Computed tomography -- Computer‐aided diagnosis -- Neuroscience -- Stroke -- Segmentation; brain -- computerised tomography -- image denoising -- image segmentation -- medical disorders -- medical image processing -- neurophysiology; computed tomography -- cerebrospinal fluid -- image segmentation -- active contour model -- intensity inhomogeneity -- local minimum; Computerised tomographs -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; Medical imaging -- Medical image segmentation -- Computed tomography -- Brain -- Biomedical modeling -- Medical image noise -- Medical image contrast -- Stroke -- Medical magnetic resonance imaging -- Experiment design; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4774359
• Languages: English
• ISSNs: 0094-2405

Abstract

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.