NDLI: Automatic detection of tumor subtype in mammograms based On GLCM and DWT features using SVM

Content Provider	IEEE Xplore Digital Library
Author	Mohamed Fathima, M. Manimegalai, D. Thaiyalnayaki, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Technol., Nat. Eng. Coll., Kovilpatti, India (Mohamed Fathima, M.; Manimegalai, D.; Thaiyalnayaki, S.)
Abstract	Mammography images are employed in diagnosing breast cancers, since they are most effective, low cost and one of the highly sensitive techniques such that they can detect even small lesions. The proposed work increases the accuracy of classification and reduces the percentage of false positives. The images from the data set are initially preprocessed and contrast enhanced which makes the image most effective for further analysis. Then Region Of Interest (ROI) is determined from morphological top hat filtered image by means of thresholding segmentation. Various features like first order textural features, Gray Level Co-occurrence Matrix (GLCM) features, Discrete Wavelet Transform (DWT) features, run length features and higher order gradient features are derived for the particular ROI. Support Vector Machine (SVM) classifier is trained with the above mentioned features using MATLAB bioinformatics tool box. Thus the classified results are obtained for the query image based on the trained SVM structure. The mammography data set has been taken from the Mammographic Image Analysis Society (MIAS) in which there are 322 images available along with ground tooth information.
Sponsorship	IEEE Madras Sect.
Starting Page	809
Ending Page	813
File Size	253428
Page Count	5
File Format	PDF
ISBN	9781467357869
e-ISBN	9781467357883
DOI	10.1109/ICICES.2013.6508213
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image segmentation Accuracy Segmentation Mammograms Filtering algorithms Feature extraction Breast cancer SVM Classifier GLCM & Run length features
Content Type	Text
Resource Type	Article

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