Brain Tumor Classification Using Enhanced Statistical Texture Features

ABSTRACT

Radiology is a vast subject and we require more knowledge and understanding for exact detection of tumor in medical science. Thus a need for tumor detection system overcomes the shortage of skilled radiologists. Biomedical image processing using Magnetic Resonance Imaging (MRI) makes the task of detection and localization of brain tumor. In this article, a brain tumor segmentation and detection approach has been designed using MRI sequence images as input image for defining the tumor region. This process is difficult due to the large diversity in the presences of tumor tissues with respect to different patients and in most of the cases similarity within the normal tissues makes the task difficult. The main goal is to classify the brain into the presence of brain tumor or a healthy brain. The proposed system provides Edge-based Contourlet Transformation for multiple input image registration, fusion and pre-processing, for Region of Interest(ROI) of tumor region the region-growing segmentation algorithm provides accurate boundaries, in feature extraction the Gray Level Run Length Matrix (GLRLM) and Center-Symmetric Local Binary Patterns(CSLBP) texture features are combined for efficient brain tumor detection and for classification Adopting Neural Network(ANN) techniques is carried out. The experimental results of our proposed method are compared with different algorithms in terms of accuracy.

KEYWORDS:

• Artificial neural network
• Center-symmetric local binary patterns
• Magnetic resonance imaging
• Gray level run length matrix

Additional information

Notes on contributors

Mallikarjun Mudda

Mallikarjun Mudda received his BTech degree in electronics and communication engineering in from Visvesvaraya Technological University, India, in 2008, and MTech degree in digital communication engineering from Visvesvaraya Technological University, India, in 2012, and he completed PhD in electronics engineering from Jain University, Bangalore, India. He is currently a faculty member of Sreenidhi Institute of Science and Technology, JNTU. His research interest areas are medical image processing, satellite image processing, digital communication, wireless network security and mobile computing.

R. Manjunath

R Manjunath has contributed to digital living network alliance (DLNA) and serves as the industrial liaison for CELinux Forum. He has chaired about 30 international conferences. His areas of interest include medical imaging, networking, signal processing, multimedia, database architecture, etc and he is presently working in Wipro Technology, Bangalore. Email: [email protected]

N. Krishnamurthy

N Krishnamurthy graduated in physics with honours from the University of Mysore and then studied postgraduate diploma in electrical technology and master of engineering in high voltage engineering, both from Indian Institute of Science, Bangalore. Furthermore, he obtained his PhD in electrical engineering from the University of London. Also he has been trained in manufacturing techniques of electrical and electronic components in Japan and has taken a course in entrepreneur development. He has industrial experience both as a top technical person in an electronic components industry and running a small scale unit in electronics for 25 years and research experience for 5 years and teaching experience for 10 years. Currently, he is working as a professor in the Department of Electrical and Electronics Engineering. He has several publications in national and international conferences and journals to his credit. Email: [email protected]