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

doi:10.1016/j.ultras.2017.02.003

Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Ultrasonics. 2017 May:77:110-120. doi: 10.1016/j.ultras.2017.02.003. Epub 2017 Feb 6.

Authors

U Raghavendra¹, U Rajendra Acharya², Anjan Gudigar³, Jen Hong Tan⁴, Hamido Fujita⁵, Yuki Hagiwara⁴, Filippo Molinari⁶, Pailin Kongmebhol⁷, Kwan Hoong Ng⁸

Affiliations

¹ Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India. Electronic address: raghavendra.u@manipal.edu.
² Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Clementi 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
³ Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India.
⁴ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore.
⁵ Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan.
⁶ Department of Electronics and Telecommunications, Politecnico di Torino, Italy.
⁷ Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
⁸ Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia.

PMID: 28219805
DOI: 10.1016/j.ultras.2017.02.003

Abstract

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.

Keywords: CAD; Classification; Fractal features; Marginal fisher analysis; Thyroid.

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Child
Diagnosis, Computer-Assisted*
Diagnosis, Differential
Female
Fractals
Humans
Image Interpretation, Computer-Assisted / methods*
Male
Middle Aged
Retrospective Studies
Sensitivity and Specificity
Support Vector Machine
Thyroid Neoplasms / diagnostic imaging*
Thyroid Neoplasms / pathology*
Ultrasonography / methods*