An Amalgam Approach for DICOM Image Classification and Recognition
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:62387", author = "J. Umamaheswari and G. Radhamani", title = "An Amalgam Approach for DICOM Image Classification and Recognition", abstract = "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", volume = "6", number = "2", pages = "260-6", }
