Abstract
This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.
Highlights
Nowadays, neurodegenerative disorders affect over 30 million people around the world
The primary goal of Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimers disease (AD)
It is worth mentioning that most relevant Regions of Interest (ROI) computed by the proposed method, specially in GM, are compatible with areas that appear in literature as representative regions of Alzheimer’s Disease (AD), located in the temporal lobe, such as hippocampus and the superior temporal gyrus which are responsible for the individual’s memory formation, speech perception, and language skills [45,46]
Summary
Neurodegenerative disorders affect over 30 million people around the world. There exist some methods that aim to detect functional brain variations [2,3,4,5,6] by exploiting the information contained in the images to learn patterns associated to cerebral damage. SOM training is performed in a competitive way so that just a single neuron wins (i.e. its prototype vector is the most similar one to the input data instance). The most similar prototype to the input data sample is called Best Matching Unit (BMU) and it is computed as: Evk{viEƒEvk{vjE Vj [ S ð1Þ where vk is the k-sample from the input space, vi is the iprototype (i.e. the weight associated the the i-unit) and S is the output SOM space.
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