Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.

[1]  Gang Wang,et al.  A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis , 2012, Journal of Medical Systems.

[2]  P DESAIVE,et al.  [Thyroid cancer]. , 1951, Revue medicale de Liege.

[3]  Joel E. W. Koh,et al.  Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images , 2016, Knowl. Based Syst..

[4]  Satnam Singh Dlay,et al.  Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition , 2017, Pattern Recognit..

[5]  Nikos Dimitropoulos,et al.  Computational Characterization of Thyroid Tissue in the Radon Domain , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[6]  Tan Yee Fan,et al.  A Tutorial on Support Vector Machine , 2009 .

[7]  U Rajendra Acharya,et al.  Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems. , 2012, Ultrasonics.

[8]  U. Rajendra Acharya,et al.  Automated Diagnosis of epilepsy using CWT, HOS and Texture parameters , 2013, Int. J. Neural Syst..

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  V. Sowmya,et al.  Scene Classification Using Transfer Learning , 2019, Recent Advances in Computer Vision.

[11]  U. Rajendra Acharya,et al.  ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform , 2012, Comput. Methods Programs Biomed..

[12]  Aytürk Keles,et al.  ESTDD: Expert system for thyroid diseases diagnosis , 2008, Expert Syst. Appl..

[13]  Cenk Sahin,et al.  A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases , 2008, Journal of Medical Systems.

[14]  Anjan Gudigar,et al.  Local texture patterns for traffic sign recognition using higher order spectra , 2017, Pattern Recognit. Lett..

[15]  Agnieszka Witkowska,et al.  A Review on Ultrasound-Based Thyroid Cancer Tissue Characterization and Automated Classification , 2014, Technology in cancer research & treatment.

[16]  Anjan Gudigar,et al.  Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions , 2017, Ultrasonics.

[17]  Yongmin Kim,et al.  Differential diagnosis of thyroid nodules with ultrasound elastography based on support vector machines , 2010, 2010 IEEE International Ultrasonics Symposium.

[18]  E. Powers,et al.  Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions , 1979, IEEE Transactions on Plasma Science.

[19]  Dimitrios K. Iakovidis,et al.  Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns , 2010, Artif. Intell. Medicine.

[20]  Jing Yu,et al.  Feature selection and thyroid nodule classification using transfer learning , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[21]  Tulay Yildirim,et al.  Diagnosis of thyroid disease using artificial neural network methods , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[22]  C. M. Lim,et al.  Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Dayou Liu,et al.  A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine , 2012, Journal of Medical Systems.

[24]  Vinod Chandran,et al.  Bispectral analysis of two-dimensional random processes , 1990, IEEE Trans. Acoust. Speech Signal Process..

[25]  Agnieszka Witkowska,et al.  Computer‐Aided Diagnostic System for Detection of Hashimoto Thyroiditis on Ultrasound Images From a Polish Population , 2014, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[26]  Kemal Polat,et al.  A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis , 2007, Expert Syst. Appl..

[27]  Esin Dogantekin,et al.  An automatic diagnosis system based on thyroid gland: ADSTG , 2010, Expert Syst. Appl..

[28]  G. Giannakis Cumulants: A powerful tool in signal processing , 1987, Proceedings of the IEEE.

[29]  Zbigniew Omiotek,et al.  Fractal analysis of the grey and binary images in diagnosis of Hashimoto's thyroiditis , 2017 .

[30]  Dimitrios K. Iakovidis,et al.  ΤND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos , 2012, Journal of Medical Systems.

[31]  Leonard Wartofsky,et al.  Staging of Thyroid Cancer , 2016 .

[32]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[33]  Vasile Palade,et al.  Class Imbalance Learning Methods for Support Vector Machines , 2013 .

[34]  Esin Dogantekin,et al.  An expert system based on Generalized Discriminant Analysis and Wavelet Support Vector Machine for diagnosis of thyroid diseases , 2011, Expert Syst. Appl..

[35]  Jianrui Ding,et al.  Quantitative Measurement for Thyroid Cancer Characterization Based on Elastography , 2011, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[36]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

[37]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[38]  Anil T Ahuja,et al.  Ultrasound of thyroid cancer , 2005, Cancer imaging : the official publication of the International Cancer Imaging Society.

[39]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2012 , 2012, CA: a cancer journal for clinicians.

[40]  Paul Babyn,et al.  Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network , 2017, Journal of Digital Imaging.

[41]  C. M. Lim,et al.  Cardiac state diagnosis using higher order spectra of heart rate variability , 2008, Journal of medical engineering & technology.

[42]  Dimitris K. Iakovidis,et al.  Computer-Based Nodule Malignancy Risk Assessment in Thyroid Ultrasound Images , 2011 .

[43]  Nikita Singh,et al.  Ultra sonogram Images for Thyroid Segmentation and Texture Classification in Diagnosis of Malignant (Cancerous) or Benign (Non-Cancerous) Nodules , 2012 .

[44]  Chuan-Yu Chang,et al.  Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images , 2010, Pattern Recognit..

[45]  Halife Kodaz,et al.  Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease , 2009, Expert Syst. Appl..

[46]  Yongmin Kim,et al.  Thyroid nodule classification using ultrasound elastography via linear discriminant analysis. , 2011, Ultrasonics.

[47]  Διονύσης Α. Κάβουρας,et al.  Morphological and wavelet features towards sonographic thyroid nodules evaluation , 2015 .

[48]  U. Rajendra Acharya,et al.  Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review , 2016, Comput. Biol. Medicine.

[49]  Savita Gupta,et al.  Computer aided thyroid nodule detection system using medical ultrasound images , 2018, Biomed. Signal Process. Control..

[50]  Aboul Ella Hassanien,et al.  Fuzzy and hard clustering analysis for thyroid disease , 2013, Comput. Methods Programs Biomed..

[51]  S Nanda,et al.  Thyroid Nodule Segmentation and Classification in Ultrasound Images , 2014 .

[52]  Dayou Liu,et al.  Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease , 2012, Journal of Medical Systems.

[53]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[54]  S Vinitha Sree,et al.  Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[55]  C. M. Lim,et al.  Higher Order Spectral (HOS) Analysis Of Epileptic EEG Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[56]  Fabián Narváez,et al.  An open access thyroid ultrasound image database , 2015, Other Conferences.

[57]  U. RAJENDRA ACHARYA,et al.  Automated Diagnosis of Normal and Alcoholic EEG signals , 2012, Int. J. Neural Syst..

[58]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.