NDLI: Texture analysis based segmentation and classification of oral cancer lesions in color images using ANN

Content Provider	IEEE Xplore Digital Library
Author	Thomas, B. Kumar, V. Saini, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., IIT Roorkee, Roorkee, India (Thomas, B.; Kumar, V.) \|\| Cancer Res. Inst., Himalayan Inst. of Med. Sci., Dehradun, India (Saini, S.)
Abstract	Features derived from Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length (GLRL) matrix are widely used for image characterization based on texture analysis. In this paper, we propose the application of suitably selected texture discriminating features for classification of oral cancer lesions in digital camera images into six groups. Backpropagation based Artificial Neural Network (BPANN) is used to compare and validate the performance of different feature sets. The classification accuracy is observed to improve with combination of GLCM, GLRL and intensity based first order features. Further improvement in accuracy is obtained by application of feature selection using boxplot analysis. A set of 61 features is formulated and applied on 192 sections of images taken from 16 patients. Such a classification of malignancies is helpful in prognosis and treatment of oral cancer which is the most common form of cancer in India.
Starting Page	1
Ending Page	5
File Size	549686
Page Count	5
File Format	PDF
e-ISBN	9781467361903
DOI	10.1109/ISPCC.2013.6663401
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Texture analysis Feature selection Color Artificial neural networks Co-occurrence matrix Run-length matrix Image segmentation Accuracy Feature extraction Artificial neural network Lesions Cancer Image classification
Content Type	Text
Resource Type	Article

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