Automated Preliminary Brain Tumor Segmentation Using MRI Images
Brain tumor has ample variations from person to person, in terms of size, texture or distribution. Experienced oncologists can easily identify the tumor region. But given its variations, training an algorithm to detect the tumor is a challenging task. Many algorithms have been developed to segment and detect the tumors, taking into consideration various aspects of an image. In this paper, we discussed a survey of brain tumor segmentation algorithms in image processing, and our proposed approach to detect brain tumors using texture and feature vectors and one-class classification methods.
Keywords: Brain Tumor, Image Segmentation, Histogram of Oriented Gradients, One Class Classification.
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ABOUT THE AUTHORS
Shamla Mantri
Presently Assistant Professor at MIT College of Engineering. Completed ME in Electronics and Telecommunication and area of interest is Digital Image Processing.
Aditi Jahagirdar
Presently Assistant Professor at MIT College of Engineering. Completed ME in Electronics and area of interest is Digital Electronics and Microprocessor, Image Processing.
Kuldeep Ghate
Currently in final year of Engineering.
Aniket Jiddigouder
Currently in final year of Engineering.
Neha Senjit
Currently in final year of Engineering.
Saurabh Sathaye
Currently in final year of Engineering.
Shamla Mantri
Presently Assistant Professor at MIT College of Engineering. Completed ME in Electronics and Telecommunication and area of interest is Digital Image Processing.
Aditi Jahagirdar
Presently Assistant Professor at MIT College of Engineering. Completed ME in Electronics and area of interest is Digital Electronics and Microprocessor, Image Processing.
Kuldeep Ghate
Currently in final year of Engineering.
Aniket Jiddigouder
Currently in final year of Engineering.
Neha Senjit
Currently in final year of Engineering.
Saurabh Sathaye
Currently in final year of Engineering.
