Multiple Sclerosis Progression Detection in Brain MRI Images using Hybrid Segmentation
DOI:
https://doi.org/10.18486/ijcsnt/5.2.066Keywords:
Magnetic Resonance Imaging (MRI), Amplitude-modulation, Frequency-modulation (AM-FM), Multiple Sclerosis (MS) Silencing Map Detection, Fuzzy C-means Clustering (FCC), Texture analysisAbstract
Multiple Sclerosis is a brain disease that forms the number of lesions in white matter of brain as the disease progresses. In this paper texture analysis is done on brain MRI Images of real data of patients to observe the progress of disease by detection. The objective of this paper is to find the progression detection by utilizing the segmentation and feature extraction techniques. The image is segmented using the AM-FM segmentation, the filtering is done by using Saliency map method and these filtered segmented features are clustered using Fuzzy C means clustering method. The paper also proposes an adaptive iterative threshold based algorithm for detection of lesion from the clustered image. The detected features are extracted using feature extraction techniques such as morphological, local binary pattern, mean and standard deviation methods. These extracted features are classified using K-NN classifier. The experimental results obtained are efficient and provides an accuracy of 97\% which helps in accurately predicting a disease. Along with detection and classification the patch based algorithm is used for reconstructing the damaged images.
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