|
Abstract
|
Article Information:
Robust Classification of Primary Brain Tumor in MRI Images using Wavelet as the Input of ANFIS
B. Rajesh Kumar and S. Karpagaiswarya
Corresponding Author: B. Rajesh Kumar
Submitted: January 13, 2014
Accepted: May 08, 2014
Published: August 20, 2014 |
Abstract:
|
This study presents a neural network based technique for automatic classification of Magnetic Resonance Images (MRI) of the brain in two categories of benign and malignant. The proposed method consists the following stages; i.e., preprocessing, tumor region segmentation, feature extraction using DWT and classification using ANFIS classifier. Preprocessing involves removing low-frequency surrounding noise, normalizing the intensity of the individual particle images. In the second stage, the fuzzy Connectedness segmentation is used for partitioning the image into meaningful regions. In feature extraction, the obtained feature connected to MRI images using the Discrete Wavelet Transform (DWT). In the classification stage, ANFIS Classifier is used to classify the subjects to normal or abnormal (benign, malignant). The proposed technique gives high-quality results for brain tissue detection and is more robust and efficient compared with other recent works.
Key words: ANFIS, classification, Wavelet Transform (DWT), fuzzy connectedness segmentation, , ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
B. Rajesh Kumar and S. Karpagaiswarya, . Robust Classification of Primary Brain Tumor in MRI Images using Wavelet as the Input of ANFIS. Research Journal of Applied Sciences, Engineering and Technology, (7): 811-816.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
