2018
DOI: 10.20535/1810-0546.2018.5.146185
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Automated Detection of Regions of Interest for Brain Perfusion MR Images

Abstract: Background. Images with abnormal brain anatomy produce problems for automatic segmentation techniques, and as a result poor ROI detection affects both quantitative measurements and visual assessment of perfusion data. Objective. This paper presents a new approach for fully automated and relatively accurate ROI detection from dynamic susceptibility contrast perfusion magnetic resonance and can therefore be applied excellently in the perfusion analysis. Methods. In the proposed approach the segmentation output i… Show more

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Cited by 3 publications
(2 citation statements)
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“…The third algorithm to be used for the analysis is also threshold-based and uses approximate anatomical brain location (AABL) as image region for threshold value calculation [19]. The image processing by applying threshold value detected in AABL region is a type of global thresholding.…”
Section: Methodsmentioning
confidence: 99%
“…The third algorithm to be used for the analysis is also threshold-based and uses approximate anatomical brain location (AABL) as image region for threshold value calculation [19]. The image processing by applying threshold value detected in AABL region is a type of global thresholding.…”
Section: Methodsmentioning
confidence: 99%
“…background and foreground. In the case of DSC perfusion data, the preprocessing step should exclude low intensity pixels (air pixels and pixels that represent non-brain tissues) and high intensity pixels (pixels that represent regions filled with cerebrospinal fluid) as background [12]. Therefore, the thresholding should be bidirectional and should provide a binary mask M x y ( , ) for processed image as follows:…”
Section: Methods Of Researchmentioning
confidence: 99%