2015
DOI: 10.1016/j.ejrnm.2015.08.001
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Segmentation of Glioblastoma Multiforme from MR Images – A comprehensive review

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Cited by 36 publications
(16 citation statements)
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“…Such techniques commonly require a practitioner to tune their parameters [7]. Other unsupervised algorithms include clustering-based techniques [15,37] (also exploiting superpixels [44]), and Gaussian mixture modeling [38].…”
Section: Brain Tumor Segmentationmentioning
confidence: 99%
“…Such techniques commonly require a practitioner to tune their parameters [7]. Other unsupervised algorithms include clustering-based techniques [15,37] (also exploiting superpixels [44]), and Gaussian mixture modeling [38].…”
Section: Brain Tumor Segmentationmentioning
confidence: 99%
“…Extraction of radiomic features from various sources of medical images also overcomes the limitation of visual image interpretation [ 16 ]. Several literature reviews show data mining and predictive analysis have widened the scope of medical imaging [ 17 , 18 , 19 , 20 ]. This can facilitate prognostic models used in oncology.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, radiologists imagine that computerized techniques will enhance the detection abilities based on the automated machine learning approaches (Laukamp et al , 2019; Amin et al , 2018). Even though more attempts are exhibited for attaining potential results in medical image evaluations, accurate segmentation with the categorization of abnormalities is a challenging part of brain tumor detection due to its inconsistent size, shape and location (Simi and Joseph, 2015). Also, the segmentation and detection of the intra tumor region in the brain MRI image is a drawback because of the low-intensity difference among tumor cells and its adjacent cells in the brain image.…”
Section: Introductionmentioning
confidence: 99%