Medical image processing is widely used in the diagnosis of diseases such as brain tumor, cancer,... more Medical image processing is widely used in the diagnosis of diseases such as brain tumor, cancer, diabetes etc. Brain tumors are abnormal and uncontrolled proliferations of cells where, its detection plays a major role. Image segmentation is a vital role in medical image processing, where clustering technique is widely used in medical application particularly for brain tumor detection in Magnetic Resonance Imaging (MRI), which produces better results with high resolution of the image. This work focuses on the detection and classification of the types of tumors namely, gliomas, meningiomas, pituitary adenomas and nerve sheath from MRI brain image. The training and test data set of MRI brain tumor image is preprocessed and an adaptive K-means clustering is used for segmentation. After the segmentation process, the Gray Level Co-occurrence Matrix and Gabor wavelet are utilized for feature extraction. The Principle Component Analysis (PCA) method is used for the feature selection to imp...
Medical image processing is widely used in the diagnosis of diseases such as brain tumor, cancer,... more Medical image processing is widely used in the diagnosis of diseases such as brain tumor, cancer, diabetes etc. Brain tumors are abnormal and uncontrolled proliferations of cells where, its detection plays a major role. Image segmentation is a vital role in medical image processing, where clustering technique is widely used in medical application particularly for brain tumor detection in Magnetic Resonance Imaging (MRI), which produces better results with high resolution of the image. This work focuses on the detection and classification of the types of tumors namely, gliomas, meningiomas, pituitary adenomas and nerve sheath from MRI brain image. The training and test data set of MRI brain tumor image is preprocessed and an adaptive K-means clustering is used for segmentation. After the segmentation process, the Gray Level Co-occurrence Matrix and Gabor wavelet are utilized for feature extraction. The Principle Component Analysis (PCA) method is used for the feature selection to imp...
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Papers by Y. Apurva