Brain forms a significant part of the human body but has a complex structure which makes it difficult to examine the brain abnormalities. Brain tumor is one of the significant diseases and various technologies are used to detect and diagnose brain tumor by non-invasive method. Magnetic Resonance Images being one of the best is directly being analyzed by the doctors for the diagnosis of tumor which is time-consuming. Automatic detection and classification of tumor makes diagnosis easy for the doctors and can provide a valuable outlook of earlier methods. The proposed method has been implemented in two different approaches, for the analysis of MR images to identify the tumor grade (Low/High). The first approach gave results of 92.5% accuracy which was found to be better than the second approach of 87.5% accuracy. This design is easy to use and the results ensure that it is efficient, and satisfying for quick detection of the grade of tumor. © 2017 IEEE.

Monica Subashini M

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Indragandhi V

Department of Energy and Power Electronics

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

An efficient non-invasive method for brain tumor grade analysis on MR images

TENCON 2017 - 2017 IEEE Region 10 Conference

A review on the recent segmentation and tumor grade classification techniques of brain Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early detection of a brain tumor and its grade is the motivation for this study. In Magnetic Resonance Imaging (MRI), the tumor might appear clear but physicians need quantification of the tumor area for further treatment. This is where the digital image processing methodologies along with machine learning aid further diagnosis, treatment, prior and post-surgical procedures, synergizing between the radiologist and computer. These hybrid techniques provide a second opinion and assistance to radiologists in understanding medical images hence improving diagnostic accuracy. This article aims to retrospect the current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma. The methodologies used for extraction and grading of tumors which can be integrated into the standard clinical imaging protocols are elucidated. Lastly, a crucial assessment of the state of the art, future developments and trends are dissertated. © 2017 Elsevier Ltd

Biomedical Signal Processing and Control

MRI based medical image analysis: Survey on brain tumor grade classification

Astrocytoma is the most common primary tumor which develops from glial cells of brain. They are generally classified as low grade (Grade I and Grade II) and high grade (Grade III and Grade IV), and these classifications are very important in clinical practice which signifies the rate of growth. Grading of astrocytoma relies on magnetic resonant images, and pathological information is also available in clinical settings. In this proposed method, we introduce a novel approach to grade the tumor using first- and second-order image statistical parameters combined with a tool termed as ‘XLMiner.’ The actual grade of astrocytoma and the predicted grade by the classifiers are compared and the accuracy of the classifiers is summarized based on the classifier-predicted output. Experimental results demonstrate the effectiveness of the method. The accuracy of Naives Bayes, discriminant analysis, regression tree, and classification tree classifiers for the prediction of grades from lower (I, II) to higher (III, IV) are 100, 81, 76, and 78% for all the views, respectively. © Springer India 2015.

Advances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

Analysis of Classification Models Using Image Statistics and Data Miner for Grade Prediction of Astrocytoma

The proposed method predicts the grade of astrocytoma based on certain features extracted from magnetic resonant images. MR brain images of three views namely Axial, Sagittal and Coronal are considered for perfect grade evaluation of astrocytoma. Gray level co-occurrence matrix is utilized for feature extraction. The extracted features are subjected to classification through a classifier which predicts the grade. Fuzzy logic classifier and K-Clustering network are the most used classifiers for prediction of grades among many available. The performances of the techniques are evaluated through discussions with neuroradiologist to find their accuracies. A modified K-Clustering network is developed to give a better diagnosis for predicting the grades of astrocytoma. © Springer India 2015.

Journal	Data powered by TypesetTENCON 2017 - 2017 IEEE Region 10 Conference
Publisher	Data powered by TypesetIEEE
ISSN	21593442
Open Access	0