S.Sakkaravarthi 1, Dr.K.G.Srinivasagan 2, S.MuthuKumar 3
1 Assistant Professor, Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.
2 Professor & Head - Department of CSE-PG, National Engineering College, Aruppukottai, Tamilnadu, India.
3 Professor & Head - Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.
1 Assistant Professor, Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.
2 Professor & Head - Department of CSE-PG, National Engineering College, Aruppukottai, Tamilnadu, India.
3 Professor & Head - Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.
Medical
imaging plays a vital role in diagnosing the diseases as well as the study of
human anatomy and physiology. MRI is an advanced medical imaging technique
especially used for capturing the human brain. The manual interpretation of
brain tumor slices based on visual examination by physician may lead to missing
diagnosis and time consuming when a large number of MRI brain images are analyzed.
To avoid human based diagnostic error, automated brain tumor classification is
preferred. For automated brain tumor classification, various techniques are
available. Those techniques suffer due to misclassification which is unfavoured
by physicians. Still the problem is open and research is going to promote
better result in very fast manner. Still the problem is open and research is
going to promote better result in very fast manner. Automated MRI Brain image
classification using Support Vector Machine classifier is proposed to classify
brain image into normal or abnormal. Brain abnormality can be further
classified into benign or malignant. In this paper, MRI brain image is
pre-processed using median filter. Then, statistical based texture features are
extracted from Gray Level Co-occurrence Matrix (GLCM) of an input brain image.
After feature extraction, relevant features are obtained using forward feature
selection technique to reduce the feature space. The selected features are
given as input to Support Vector Machine classifier. Finally, Support vector
machine classifier is utilized to perform two functions. The first is to
differentiate between normal and abnormal. The second function is to classify
the type of abnormality in benign or malignant tumor.
