International Journal of Scientific and Research Publications

IJSRP, Volume 6, Issue 6, June 2016 Edition [ISSN 2250-3153]


Development of MRI Brain Image Segmentation- technique with Pixel Connectivity
      S. Abdalla and N. Al-Aama, Maryam A. Al-Ghamdi
Abstract: Recently, Magnetic Resonance Imaging (MRI) of Brain is used widely in the clinical applications for the detection of abnormalities such as tumor. Accurate segmentation of the affected regions in the brain MRI image plays a vital role in the quantitative image analysis to detect the location of tumor in the brain. However, many segmentation algorithms suffer from limited accuracy, due to the presence of noise and intensity inhomogeneity in the brain MR images. This paper proposes a novel Textural Pixel Connectivity (TPC) based segmentation technique to predict the location of brain tumor. The Probabilistic Neural Network (PNN) classifier is used to classify the normal and abnormal images. If the image is classified as abnormal, then TPC segmentation process is applied for clustering out the background and tumor spot in the binary segmented output. Then, the growing pattern of tumor is analyzed and represented as a binary image output. The proposed technique achieves superior performance in terms of sensitivity, specificity, accuracy, error rate, correct rate, inconclusive rate, Positive Predicted Values (PPV), Negative Predicted Values (NPV), classified rate, prevalence, positive likelihood and negative likelihood, when compared to the traditional Adaboost and Enhanced Adaboost techniques.

Reference this Research Paper (copy & paste below code):

S. Abdalla and N. Al-Aama, Maryam A. Al-Ghamdi (2018); Development of MRI Brain Image Segmentation- technique with Pixel Connectivity; Int J Sci Res Publ 6(6) (ISSN: 2250-3153). http://www.ijsrp.org/research-paper-0616.php?rp=P545503
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