Abstract
This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.
Highlights
Recent advances in neuropathology have significantly facilitated research into the underlying physiology in the advancement of cognitive impairment. is disorder is related to irregular protein buildup in the cerebrum, which prompts neuronal impairment in the synapses, nerve cells, and axons
E delayed symptoms of dementia are due to dissipation of cognitive reserve in terms of numbers of undamaged neurons, which result in loss of memory function only when decreasing below a certain limit
Changes in the size or volume of these regions can be correlated to changes in cerebral structure in patients with Alzheimer’s, dementia, cognitive impairment, or other neurological disorders
Summary
Recent advances in neuropathology have significantly facilitated research into the underlying physiology in the advancement of cognitive impairment. is disorder is related to irregular protein buildup in the cerebrum, which prompts neuronal impairment in the synapses, nerve cells, and axons. Structural anatomical changes in regions of the medial temporal lobe measured from brain MRI scans of patients taken over several dates can be used to estimate the rate of atrophy. Complete cerebral [15,16,17,18,19], entorhinal cortical [20], hippocampal [9,21,22,23], and temporal [24, 25] lobe volume atrophy rates and swelling of ventricular regions [9, 21, 23, 26] both correspond to cognitive impairment, validating their legitimacy as measures of dementia. Future work would entail validating the accuracy of the cortex thickness measurements using distance across the boundaries and testing for robustness of the algorithm over varying image acquisition systems with changing scanner type, signal to noise ratio, and number of MRI slices captured [50, 51]
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