Research Article
Thyroid Volume Estimation Analysis with Neural and Fuzzy-Neuro Techniques - A Comparative Study
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 61 - Issue 16 |
| Published: January 2013 |
| Authors: M. Balakarthikeyan, B. Chidambararajan |
10.5120/10012-4949
|
M. Balakarthikeyan, B. Chidambararajan . Thyroid Volume Estimation Analysis with Neural and Fuzzy-Neuro Techniques - A Comparative Study. International Journal of Computer Applications. 61, 16 (January 2013), 24-27. DOI=10.5120/10012-4949
@article{ 10.5120/10012-4949,
author = { M. Balakarthikeyan,B. Chidambararajan },
title = { Thyroid Volume Estimation Analysis with Neural and Fuzzy-Neuro Techniques - A Comparative Study },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 61 },
number = { 16 },
pages = { 24-27 },
doi = { 10.5120/10012-4949 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A M. Balakarthikeyan
%A B. Chidambararajan
%T Thyroid Volume Estimation Analysis with Neural and Fuzzy-Neuro Techniques - A Comparative Study%T
%J International Journal of Computer Applications
%V 61
%N 16
%P 24-27
%R 10.5120/10012-4949
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a comparative study of various methods that used to identifies the thyroid volume from the ultrasound images. The Radial basis function neural network method, and by the hybrid structure of neural network and the fuzzy logic method. The performance of the algorithms Active Contour without edges, Localized region Based active contour and Distance Regularized Level Set will improve the results by 80%, the hybrid structure of the neuro – fuzzy method was improved from 81% to 92. 9% by using Fuzzy-RBF, Fuzzy-MLP, and Fuzzy-CSFNN methods. The result shows that the Fuzzy Neuro method gives the highest accuracy level the by using the neural network method for detecting the thyroid volume from the segmentation process.
References
- Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng, and Shyang-Rong Shih. THYROID SEGMENTATION AND VOLUME ESTIMATION IN ULTRASOUND IMAGES. IEEE Transaxtions on Biomedical Engineering, June 2010, vol 57,No 6.
- I. S. Isa, Z. Saad, S. Omar, M. K. Osman, K. A. Ahmad, H. A. MatSakim, 2010, SUITABLE MLP NETWORK ACTIVATION FUNCTIONS FOR BREAST CANCER AND THYROID DISEASE DETECTION, Second International Conference on Computational Intelligence.
- Canan SENOL, Tulay YILDIRIM, THYROID AND BREAST CANCER DISEASE DIAGNOSIS USING FUZZY-NEURAL NETWORKS. Nov 2009, IEEE International Conference on Electrical & Electronics Engineering.
- Nikita Singh, Alka Jindal, A SEGMENTATION METHOD AND COMPARISON OF CLASSIFICATION METHODS FOR THYROID ULTRASOUND IMAGES, july 2012, International Journal of Computer Applications, Vol 50 – No. 11.
- JaspreetKaur, Alka Jindal, COMPARISON OF THYROID SEGMENTATION ALGORITHMS IN ULTRASOUND AND SCINTIGARPHY IMAGES, july 2012, International Journal of Computer Applications, Vol 50 – No 23.
- EystrationsG. Keramidas, DimitrisK. Iakovidis, DimitrisMaroulis, THYROID TEXTURE REPRESENTATION VIA NOISE RESISTANT IMAGE FEATURES, 2008, 21st IEEE International Symposium on Computer Based Medical Systems.
- Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng, and Shyang-Rong Shih. THYROID SEGMENTATION AND VOLUME ESTIMATION IN ULTRASOUND IMAGES, 2008, IEEE International Conference on Systems, Man and Cybernetics.
- Eva N. K. Kollorz, Dieter A. Hahn, Rainer Linke, TmmeW. Goecke, Joachim Hornegger, and torstenKumert, QUANTIFICATION OF THYROID VOLUME USING 3-D ULTRASOUND IMAGING, APRIL 2008, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol 27 – No 4.
- MichalisA. Savelonas, DimitrisK. Lakovidis, IoannisLegakis, ACTIVE CONTOURS GUIDED BY ECHOGENICITY AND TEXTURE FOR DELINEATION OF THYROID NODULES IN ULTRASOUND IMAGES, July 2009, IEEE Transations on Information Technology in Bioscience, Vol 13 – No 4.
- FatemehSaiti, AfsanehAlaviNaini, Mahdi AliyariShoorehedeli, Mohammad Teshnehlab, THYROID DISEASE DIAGNOSIS BASED ON GENETIC ALGORITHMS USING PNN AND SVM, 2009, 3rd International Conference on Bioinformatics and Biomedical Engineering.
- Jieming Ma, Si Luo, ManjiriDighe, Dong – Jun Lim, and Yongmin Kim, DIFFERENTIAL DIAGNOSIS OF THYROID NODULES WITH ULTRASOUND ELASTOGRAPHY BASED ON SUPPORT VECTOR MACHINES, 2010, IEEE, International Ultrasonics Symposium Proceedings.
- Roberta Carraro, Filippo Molinari, and MaurilioDeandrea, CHARACTERIZATION OF THYROID NODULES BY 3-D CONTRAST-ENHANCED ULTRASOUND IMAGING, 2008, 30th Annual International IEEE EMBS Conference.
- Chhuan-Yu Chang, Hsiang-Yi Liu, Chin-Hsiao Tseng, and Shyang-Rong Shih, AUTOMATIC DIAGNOSIS OF THYROID GRAVES' DISEASE IN ULTRASOUND IMAGES, 2009, 9th International Conference on hybrid Intelligent Systems.
- Si Luo, Eung-Hun Kim, ManjiriDighe and Yongmin Kim, SCREENING OF THYROID NODULES BY ULTRASOUND ELASTOGRAPHY USING DIASTOLIC STRAIN VARIATION, 2009, 31st Annual International Conference of the IEEE EMBS Conference.
- N. Hu, D. B. Downey, A. Penster and H. M. Ladak,PROSTATE BOUNDARY SEGMENTATION FROM 3D ULTRASOUND IMAGES, Medical Physics, Vol 30. No. 7, pp. 1648-1659, july 2003.
- T. Loupas, W. N. McDicken and P. L. Allan, AN ADAPTIVE WEIGHTED MEDIAN FILTER FOR SPECKLE SUPPRESSION IN MEDICAL ULTRASOUNIC IMAGES, IEEE Transactions on Circuits Synthesis, Vol. 36, pp. 129-135, Jan 1989.
- C. H. Lee, Y. C. Lin, HYBRID LEARNING ALGORITHM FOR FUZZY NEURO SYSTEMS, IEEE International Conference on Fuzzy Systems, Budapest, Hungary, July 2004.
Index Terms
Keywords