Abstract
Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall.
Highlights
Magnetic Resonance Image (MRI) gives internal visualization of soft tissues of brain and analysis if MRI is from plentiful visual information when expert when wants to examine brain for detection of brain tumor
Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; contour has been generated
Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall
Summary
MRI gives internal visualization of soft tissues of brain and analysis if MRI is from plentiful visual information when expert when wants to examine brain for detection of brain tumor. Two kind of brain tumor have been seen in images like benign and second one is malignant. Experts check type of tumor with boundary of tissue in MRI. Three orientation of MRI are available for visualization like Sagittal (x axis), coronal (Y axis) and Axil (Z axis). In this paper Axil slice of T2 give more highlight of tumor boundary but challenge to detect due to homogenous intensity. Experts interested observing brain tumor from digital images which are noisier. To identify information from these digital images, the process of segmentation has been used. Automatic segmentation using different method becomes important and challenging for more accurate detection. Segmentation improves with combination of thirteen texture and statistical features, reduction of features and classification to segment target labels
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