Abstract
Background: Recently, Magnetic Resonance Imaging (MRI) of brain is used widely in the clinical applications for the detection of abnormalities such as tumor.
Methods: 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 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. Results & Conclusion: 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.Keywords: Affine transform, brain tumor detection, canny-based edge detection, fractional fourier transform (FRFT), gaussian mixture model (GMM), improved gray level co-occurrence matrix (I-GLCM), magnetic resonance imaging (MRI) Brain Image, probabilistic neural network (PNN) classifier, textural pixel connectivity (TPC) based segmentation.
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