Automatic white matter lesion segmentation using an adaptive outlier detection method

Magnetic Resonance Imaging
Kok Haur OngIbrahim Lutfi Shuaib

Abstract

White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the...Continue Reading

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Citations

May 15, 2013·Journal of Neurosurgery. Pediatrics·David S HershSarah S Milla
May 21, 2013·Magnetic Resonance Imaging·Rita SimõesCornelis Slump
Apr 8, 2015·IEEE Transactions on Medical Imaging·Carole H SudreSebastien Ourselin
Jun 13, 2017·NeuroImage·Mahsa DadarUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Apr 5, 2014·Stroke; a Journal of Cerebral Circulation·Yogheswaran GopalanKah Hay Yuen
Jun 4, 2021·Scientific Reports·Subhranil KoleyIman Aganj
Jul 30, 2021·Medical Image Analysis·Vaanathi SundaresanLudovica Griffanti

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