The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
%0 Journal Article
%1 noauthororeditor
%A Padma, A.
%A Sukanesh, Dr.R.
%D 2011
%J International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)
%K (ROC) (SGLDM) Algorithm(GA) Characteristic Dependence Discrete Genetic Gray Level Machine(SVM) Method Operating Receiver Spatial Support Transform(DWT) Vector Wavelet analysis
%N 3
%P 22-34
%R 10.5121/ijcseit.2011.1303
%T Automatic Diagnosis of Abnormal Tumor Region
from Brain Computed Tomography Images Using
Wavelet Based Statistical Texture Features
%U http://airccse.org/journal/ijcseit/papers/0811ijcseit03.pdf
%V 1
%X The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
@article{noauthororeditor,
abstract = {The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.},
added-at = {2018-11-13T06:06:09.000+0100},
author = {Padma, A. and Sukanesh, Dr.R.},
biburl = {https://www.bibsonomy.org/bibtex/2262fa153f98236ce4edc8bcbba6d4893/ijcseit},
doi = {10.5121/ijcseit.2011.1303},
interhash = {744cd3b4b5cea804a33d914898f4f9ff},
intrahash = {262fa153f98236ce4edc8bcbba6d4893},
issn = {2231-3117 [Online] ; 2231-3605 [Print]},
journal = {International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)},
keywords = {(ROC) (SGLDM) Algorithm(GA) Characteristic Dependence Discrete Genetic Gray Level Machine(SVM) Method Operating Receiver Spatial Support Transform(DWT) Vector Wavelet analysis},
language = {English},
month = aug,
number = 3,
pages = {22-34},
timestamp = {2018-11-13T06:06:09.000+0100},
title = {Automatic Diagnosis of Abnormal Tumor Region
from Brain Computed Tomography Images Using
Wavelet Based Statistical Texture Features
},
url = {http://airccse.org/journal/ijcseit/papers/0811ijcseit03.pdf},
volume = 1,
year = 2011
}