Segmentation and Classification Algorithms for Brain Tumor Detection
A Novel Approach
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Brain Tumor is a complex disease that occurs due to the abnormal growth of brain cells. For efficient treatment planning, earlier detection of tumor is necessary. Magnetic Resonance Imaging (MRI) is now recognized as an important tool for the detection of Brain tumor. Computer Aided Diagnosis (CAD) could be almost as effective as double reading by providing a second opinion to the radiologist and help in increasing the sensitivity and accuracy of detection. A novel algorithm for brain MRI segmentation using K-Means Clustering and Texture Pattern Matrix is proposed in this work. K-Means cluster...
Brain Tumor is a complex disease that occurs due to the abnormal growth of brain cells. For efficient treatment planning, earlier detection of tumor is necessary. Magnetic Resonance Imaging (MRI) is now recognized as an important tool for the detection of Brain tumor. Computer Aided Diagnosis (CAD) could be almost as effective as double reading by providing a second opinion to the radiologist and help in increasing the sensitivity and accuracy of detection. A novel algorithm for brain MRI segmentation using K-Means Clustering and Texture Pattern Matrix is proposed in this work. K-Means clustering with Texture Pattern Matrix (TPM) based segmentation process is implemented to detect Brain Tumor. In this book, Region growing, Watershed and Active Contour Model (ACM) are implemented to authenticate the performance of the proposed method. Fuzzy C-means (FCM) algorithm is also implemented and it is combined with TPM to evaluate the performance of the segmentation algorithm. The parameters used to evaluate the performance of segmentation are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Accuracy, Correlation, Dice Coefficient, and Jaccard Index.
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