Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions
© 2017 Elsevier B.V. Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but...
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th-cmuir.6653943832-471812018-04-25T07:25:04Z Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions U. Raghavendra U. Rajendra Acharya Anjan Gudigar Jen Hong Tan Hamido Fujita Yuki Hagiwara Filippo Molinari Pailin Kongmebhol Kwan Hoong Ng © 2017 Elsevier B.V. Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database. 2018-04-25T07:25:04Z 2018-04-25T07:25:04Z 2017-05-01 Journal 0041624X 2-s2.0-85013131089 10.1016/j.ultras.2017.02.003 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85013131089&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/47181 |
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© 2017 Elsevier B.V. Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database. |
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Journal |
author |
U. Raghavendra U. Rajendra Acharya Anjan Gudigar Jen Hong Tan Hamido Fujita Yuki Hagiwara Filippo Molinari Pailin Kongmebhol Kwan Hoong Ng |
spellingShingle |
U. Raghavendra U. Rajendra Acharya Anjan Gudigar Jen Hong Tan Hamido Fujita Yuki Hagiwara Filippo Molinari Pailin Kongmebhol Kwan Hoong Ng Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
author_facet |
U. Raghavendra U. Rajendra Acharya Anjan Gudigar Jen Hong Tan Hamido Fujita Yuki Hagiwara Filippo Molinari Pailin Kongmebhol Kwan Hoong Ng |
author_sort |
U. Raghavendra |
title |
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
title_short |
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
title_full |
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
title_fullStr |
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
title_full_unstemmed |
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
title_sort |
fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions |
publishDate |
2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85013131089&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/47181 |
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