Automatic extraction of cortical gray matter by geodesic contours

İskurt, Ali; Becerikli, Yaşar

doi:10.1109/icat.2011.6102104

Cited by 1 publication

(4 citation statements)

References 15 publications

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“…Circularity, being a long closed contour, and forming a railway pattern with adjacent geodesics are the features that make geodesics valuable for cortical tissue segmentation because the GM-WM boundary exactly possesses these three properties. These geodesics are not active or evolving as mentioned in two recent two studies [15,16].…”

Section: Introductionmentioning

confidence: 98%

“…cavities and geodesics, all added. Final GM-MW tissue separation in (h) and (i) [15]. Figure 4 is elucidative for better understanding the above algorithm.…”

Section: Extraction Of Gm-wm Boundary With Fsgpc-iimentioning

confidence: 99%

“…Thus, nonbrain tissues must be removed by a skull-stripping algorithm [15]. Comparison of the brain extraction algorithms can be found in [17].…”

Section: Underlying Algorithmsmentioning

confidence: 99%

“…This is done by utilizing geodesic passive contours (GPCs) with two novel algorithms first presented at a conference [15]. The sensitiveness of human eyes to the edges is imitated and a seamless border is extracted without getting confused with the regional variation of the intensity.…”

Section: Introductionmentioning

confidence: 99%

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Automated separation of gray and white matter in brain MRIs by fastened segments of geodesic contours

İskurt¹

2016

Turk J Elec Eng & Comp Sci

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Gray matter (GM) and white matter (WM) are adjacent tissues in the brain separated by an interface.Extraction of this boundary is very important for quantitative analysis and monitoring of atrophy. A perfect capture for this purpose is a missing vital item for which research continues. In order to get close to such precision, two novel systems are presented for segmentation of cortical GM and WM in brain MRIs. The system imitates human perceptual sensitiveness to contrast, which is the basic principle of edge detection algorithms, and completes its shortcoming feature of segmentation. As well as being completely automatic, the system is also unsupervised. The correct GM-WM boundary rate gets close to 77% and the segmentation accuracy is over 95%, which are promising results. In comparison tests with SPM, the proposed technique showed 6% higher accuracy with both noisy and normal data and better recovery of small cavities in sulci, being confirmed by experts' drawings.

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Section: Introductionmentioning

confidence: 98%

“…cavities and geodesics, all added. Final GM-MW tissue separation in (h) and (i) [15]. Figure 4 is elucidative for better understanding the above algorithm.…”

Section: Extraction Of Gm-wm Boundary With Fsgpc-iimentioning

confidence: 99%

“…Thus, nonbrain tissues must be removed by a skull-stripping algorithm [15]. Comparison of the brain extraction algorithms can be found in [17].…”

Section: Underlying Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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