3D brain segmentation using active contour with multi labeling method

Salman, Saif Dawood; Habash, Qais Ahmed; Ahmed, Zainab Thabit

doi:10.1109/nces.2012.6740480

Cited by 1 publication

(1 citation statement)

References 11 publications

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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].…”

Section: Differences With Related Workmentioning

confidence: 99%

A fast segmentation-free fully automated approach to white matter injury detection in preterm infants

Mukherjee

Cheng

Miller

et al. 2018

Med Biol Eng Comput

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White matter injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in magnetic resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear maximally stable extremal regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy. Graphical Abstract Key Steps of Segmentation-free WMI Detection.

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