Brain Tumour Segmentation using Digital Image Processing
Prof. Dr. Rubeena Vohra, Bhavika, Hansika, Palvika, Swati
ECE Department, Bharati Vidhyapeeth’s College of Engineering, GGSIP, New Delhi, India
Abstract—Brain tumour is a serious life- altering disease condition. It occurs by means of abnormal cells which form within the brain. Tumour Detection is one of the most important methods used in image processing. In the past few years, numerous techniques have been proposed. In this paper, we presented an abstract automated, and accurate method to classify a given MR brain image as normal or abnormal. The proposed method first employs high pass filters for noise removal from images, followed by applying medium pass filters to enhance the quality of the image. The extracted features were submitted to the segmentation technique followed by morphological filtering which avoids the unclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumour location. Medical Science in Image Processing is an emerging field that has proposed a lot of advanced techniques in the detection and analysis of a particular disease. Treatment of brain tumours in recent years is getting more and more challenging due to the complex structure, shape, and texture of the tumour.
Keywords—: Magnetic Resonance Imaging, Computed Tomography, Red Green Blue, Support Vector Machine
I. INTRODUCTION
This paper proposes different methodologies to segment a tumour from an MRI image and determines correlation for all of the methodologies except one. For this several
segmentation techniques have been implemented and analysis is provided regarding the efficiency of the segmentation technique used. Each MRI image is passed through an imaging chain where the image is preprocessed to remove noise and is further enhanced to improve the contrast of the image. This paper proposes different segmentation techniques which are then applied to the image to extract the tumour
