The week's pick
Reseach Article
Classification of Biomedical Images using Counter Propagation Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 10 |
| Year of Publication: 2018 |
| Authors: Sangita A. Dubey, Vijay R. Rathod |
10.5120/ijca2018917714
|
Sangita A. Dubey, Vijay R. Rathod . Classification of Biomedical Images using Counter Propagation Neural Network. International Journal of Computer Applications. 182, 10 ( Aug 2018), 23-27. DOI=10.5120/ijca2018917714
Abstract
In this paper, method for the segmentation, feature extraction & classification for ultrasonic biomedical images are proposed The method has been applied & implemented Some sets of descriptors corresponding to texture measurements and geometrical features is extracted for each segmented object and applied to a classifier. The paper reports comparative results of classification of ultrasound images; research experimentation has been implemented on various ultrasound images from the standard datasets. The present experimental study is aimed at comparing the evaluation of image processing algorithms and classification techniques applied on the ultrasound images. Different image segmentation techniques have been proposed here for feature extraction. Currently, there is a large amount of research work going on in the field of automated system for inspection, analysis of ultrasound enhancement, feature extraction and classification. Comparison of other classification methods and application of image processing on the ultrasound images of are aimed to categorize the US images for medical diagnosis purpose to make accept/reject decisions as per the international biomedical standards. The diagnosis scheme includes three steps: Preprocessing, feature extraction and classification. Finally experimental results are reported for three different classifiers (Support Vector Machine, ANFIS,CPNN).
References
- Hwanga Y N, Lee JH, Kim GY, Jiang YY and Kim SM “Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network”, Bio-Medical Materials and Engineering, Shanghai-China , pp. S1599–S1611, 2015
- E.Setiawati, Adiwijaya and Tjokorda A.B.W. “Particle Swarm Optimization on Follicles Segmentation to Support PCOS Detection” 2015 3rd International Conference on Information and Communication Technology (ICoICT).pp.371-376,2015.
- Qiangzhi Zhang, Xia Zhao, Qinghua Huang, “A Multi-Objectively-Optimized Graph-Based Segmentation Method for Breast Ultrasound Image”, IEEE, pp. 116-120,2014.
- Qiangzhi Zhang, Xia Zhao, Qinghua Huang” A Multi-Objectively-Optimized Graph-Based Segmentation Method for Breast Ultrasound Image”, IEEE, pp. 116-120,2014.
- K. Binaee, R.P.R. Hasanzadeh, “An ultrasound image method using local gradient based fuzzy similarity”, Biomedical enhancement Signal Processing and control 13, Elsevier, pp. 89–101,2014.
- Okuwobi Idowu, Paul Yonghua Lu, “Image Denoising Using Wavelet Thresholding Techniques”, International Journal of Education and Research Vol. No. 2 February 2014page no. 1-5.
- Haijuan Hu,, Bing Li, Quansheng Liu , “Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means”, Preprint submitted to Elsevier arXiv:1403.2482v1 [cs.CV] 11 Mar 2014 page no. 1-29.
- Hossein Talebi, Student Member, IEEE, and Peyman Milanfar, Fellow, IEEE: “Global Image Denoising” IEEE Transactions On Image Processing, VOL. 23, NO. 2, FEBRUARY 2014 page no. 755-768.
- A.P. Dhawan, Medical Image Analysis.: John Wiley Publication and IEEE Press, 2003.
- Deepa Parasar "Ultrasound Image Enhancement using ImprovedFixed Point Independent Component Analysis," International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) - 2015.
- IoannisValavanis,et al,“Multiclass defect detection and classification in weld radiographic images using geometric and texture features” Expert Systems with Applications Volume 37, Issue 12, Pages 7606-7614 July, 2010.
- H. Kasban, et.al,” Welding defect detection from radiography images with a cepstral approach”, NDT and E International, doi: 10.1016 (In the press October 2010).
- N.M.Nanditha, et al, “Comparative study on the suitability of feature extraction techniques for tungusten inclusion and hotspot detection from weld thermographs for online weld montoring.” International Journal of Engineering and Material science, Vol. 17 pp. 20-26, February 2010.
- S. Margret Anouncia, et al “A knowledge model for gray image interpretation with emphasis on welding defect classification,” Computer Industry, vol. 61, pp. 742-749, October 2010.
- P.N.Jebarani et al, “Automatic detection of weld defects in pressure vessels using fuzzy neural network,” International Journal of Computer Applications, vol. 1, no. 39, pp. 0975-8887, 2010.
- P.N.Jebarani et al, “Automatic detection of porosity and slag inclusion in boiler using statistical pattern recognition technique ,” International Journal of Computer Applications, vol. 1, no. 39, pp. 0975-8887, 2010.
- Rafael Vilar, et al, “An automatic system of classification of weld defects in radiographic images”, NDT & E International Volume 42, Issue 5, Pages 467-476 July 2009.
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