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An Efficient Technique to Segment the Tumor and Abnormality Detection in the Brain MRI Images Using KNN Classifier

Author(s): K. S. Angel Viji, D. Hevin Rajesh

In the analysis of brain Magnetic Resonance Images (MRI), classification of normality and abnormality is an important issue. Many works have been done to classify the brain MR images. This work presents a new technique to classify the brain MRI images by using segmentation and KNN classifier. Initially, the brain MRI images obtained from brain databases are preprocessed using the Gaussian filter and the pre-processed images are normalized. Subsequently, the normalized images are subjected to segmentation by employing texture and intensity oriented region growing technique (TIORGW). Then texture features are extracted from the segmented brain MRI images. Later that, the well-known optimization algorithm called Genetic Algorithm (GA) is utilized to select the optimal texture features. Following that, the optimal features are passed in to KNN in order to classify whether the brain MRI image is normal or not. The proposed technique is implemented in the working platform of MATLAB and the performance is analysed by utilizing more number of brain MRI images.

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Citations : 9398

International Journal of Chemical Sciences received 9398 citations as per Google Scholar report

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  • Google Scholar
  • Open J Gate
  • China National Knowledge Infrastructure (CNKI)
  • Cosmos IF
  • Geneva Foundation for Medical Education and Research

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