Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
%0 Journal Article
%1 noauthororeditor
%A Pandian, A. Anbarasa
%A Balasubramanian, R.
%D 2015
%J International Journal in Foundations of Computer Science & Technology ( IJFCST )
%K CBIR Contourlet Curvelet Deep Local Texture network neural pattern ternary
%N 6
%P 14
%R :10.5121/ijfcst.2015.5604
%T PERFORMANCE ANALYSIS OF TEXTURE IMAGE
RETRIEVAL FOR CURVELET, CONTOURLET
TRANSFORM AND LOCAL TERNARY PATTERN
USING MRI BRAIN TUMOR IMAGE
%U https://wireilla.com/papers/ijfcst/V5N6/5615ijfcst04.pdf
%V 5
%X Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
@article{noauthororeditor,
abstract = {Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.},
added-at = {2023-05-11T15:02:44.000+0200},
author = {Pandian, A. Anbarasa and Balasubramanian, R.},
biburl = {https://www.bibsonomy.org/bibtex/2c627b9711c2c01c31c943492d9903d4c/devino},
doi = {:10.5121/ijfcst.2015.5604},
interhash = {6deabf13e9daea26b44a9dd5873d3dd9},
intrahash = {c627b9711c2c01c31c943492d9903d4c},
issn = {1839-7662},
journal = { International Journal in Foundations of Computer Science & Technology ( IJFCST )},
keywords = {CBIR Contourlet Curvelet Deep Local Texture network neural pattern ternary},
language = {ENGLISH},
month = nov,
number = 6,
pages = 14,
timestamp = {2023-05-11T15:02:44.000+0200},
title = {PERFORMANCE ANALYSIS OF TEXTURE IMAGE
RETRIEVAL FOR CURVELET, CONTOURLET
TRANSFORM AND LOCAL TERNARY PATTERN
USING MRI BRAIN TUMOR IMAGE
},
url = {https://wireilla.com/papers/ijfcst/V5N6/5615ijfcst04.pdf},
volume = 5,
year = 2015
}