An Amalgam Approach for DICOM Image Classification and Recognition
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32918
An Amalgam Approach for DICOM Image Classification and Recognition

Authors: J. Umamaheswari, G. Radhamani


This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2239


[1] Hsian-Min Chen, Shih-Yu Chen, Jyh Wen Chai, Clayton Chi-Chang Chen, Chao-Cheng, San-Kan Lee, Chein-I Chang, Techniques for Automatic Magnetic Resonance Image Classification, IEEE Fourth International Conference on Genetic and Evolutionary Computing IEEE computer society, P.235-238,2010.
[2] A. Ben Hamza and Hamid Krim, Image Denoising: A Nonlinear Robust Statistical Approach, IEEE Transactions on Signal Processing, Vol. 49, No. 12, P. 3045-3053, 2001.
[3] R.Hossein, V.Mansur, A.Purang, and G.Saeed, Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Prior", IEEE transactions on biomedical engineering , Vol. 55, No. 9, P. 2152-2160, 2008.
[4] J. Rajan, K. Kannan, and M.R.Kaimal, An Improved Hybrid Model for Molecular Image Denoising, Journal of Mathematical Imaging and Vision, Vol. 31, No.1, P. 73-79, 2008.
[5] Y.Yu, and S.T.Acton, Speckle Reducing Anisotropic Diffusion, IEEE Transactions on Image Processing, Vol. 11, No. 11, pp. 1260 - 1270, 2002.
[6] Yong-Hwan Lee, and Sang-Burm Rhee, Wavelet-based Image Denoising with Optimal Filter, International Journal of Information Processing Systems Vol.1, No.1, P. 32-35,2005.
[7] L.Qiang, Y.Zhengan, K.Yuanyuan, Solutions of Fourth-order Partial Differential Equations in a noise removal model, Electronic Journal of Differential Equations, No.120, P. 1-11, 2007.
[8] Nezamoddin N. Kachouie, Anisotropic Diffusion for Medical Image Enhancement, International Journal Of Image Processing (IJIP), Vol.4,No.4, P. 436-443,2010.
[9] Shujun Fu, Qiuqi Ruan, Wenqia Wang and Yu Li, Feature Preserving Nonlinear Diffusion for Ultrasonic Image Denoising and Edge Enhancement, World Academy of Science, Engineering and Technology, No. 2, P.148-151, 2005.
[10] I. Shanthi, Dr. M.L. Valarmathi, Speckle Noise Suppression of SAR Image using Hybrid Order Statistics Filters, International Journal of Advanced Engineering Sciences and Technlogies Vol No. 5, Issue No. 2, 229 - 235, 2011.
[11] Zhifeng Wang; Yandong Tang, Yingkui Du, Noise Removal Based on Fourth Order Partial Differential Equation, International conference ICICIC 2008, P.529-524, 2008.
[12] YANG Feng,SUN Xiaohuan, CHEN Guoyue,WEN Tiexiang,An Improved Hybrid Model for Medical Image Segmentation, ÏEEE, ICCS 2008, 367-370,2008.
[13] Hua Lia,b , Anthony Yezzia, A hybrid medical image segmentation approach based on dual-front evolution model", IEEE, 2005.
[14] Dr. H.B.Kekre, Sudeep D. Thepade, Tanuja K. Sarode and Vashali Suryawanshi, Image Retrieval using Texture Features extracted from GLCM, LBG and KPE, International Journal of Computer Theory and Engineering, Vol. 2, No. 5,P.1793-8201,2010.
[15] Mustafa, M. Taib, M.N., Murat, Z.H., Hamid, N.H.A.m, GLCM texture classification for EEG spectrogram image International Conference IECBES 2010, P.373-376, 2010.
[16] Dr. H.B.Kekre, Sudeep D. Thepade, Tanuja K. Sarode and Vashali Suryawanshi, Image Retrieval using Texture Features extracted from GLCM, LBG and KPE, Vol. 2, No. 5, P.1793-8201, 2010.
[17] J. Umamaheswari, Dr. G. Radhamani, A Hybrid Approach for Classification of DICOM Image, World of Computer Science and Information Technology Journal (WCSIT) , Vol. 1, No. 8,P.364-369, 2011.
[18] B. Ramamurthy, K. R. Chandran, S. Aishwarya, P. Janaranjani, CBMIR: Content Based Image Retrieval using Invariant Moments, GLCM and Grayscale Resolution for Medical Images, European Journal of Scientific Research Vol.59, No.4,. 460-471, 2011.
[19] Ergen, B. Baykara, M., Feature extraction of using statistical spatial methods for content based medical image retrieval, IEEE Bio medical imaging meeting, P.1-4,2010.
[20] Bo Qiu, Chang Sheng Xu, Qi Tian, An Automatic Classification System Applied In Medical Images, IEEE, ICME, P1045-1048,2006.
[21] Losada, C. M. Mazo, S. E. Palazuelos, and F. Redondo, Adaptive threshold for robust segmentation of mobile robots from visual information of their own movement, IEEE International Symposium on Intelligent Signal Processing,P.293-298, 2009.
[22] H. Dallaki, A. Sami, A. Hamzeh and S. Hashemi Using Feature Selection for Speed up Hybrid PSO/ACO, Journal of Computing, Vol. 3, No. 1, P. 54-60,2011.
[23] Chung-Jui Tu, Li-Yeh Chuang, Jun-Yang Chang, and Cheng-Hong Yang, Feature Selection using PSO-SVM, IAENG International Journal of Computer Science, 2007.
[24] Akinori Hidaka, Takio Kurita, Non-Neighboring Rectangular Feature Selection using Particle Swarm Optimization, IEEE Proceeding Pattern Recognition,P.1-4, 2008.
[25] Cheng-Lung Huang ,Chieh-Jen Wang, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications,Vol. 31 P. 231–240,2006.