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Ganesan, P.
- Retinal Blood Vessels and Optical Disc Segmentation in Branch Retinal Vein Occluded Fundus Images Using Digital Image Processing Techniques
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Authors
P. Ganesan
1,
B. S. Sathish
2,
L. M. I. Leo Joseph
3,
K. M. Subramanian
4,
V. Kalist
5,
K. Vasanth
1
Affiliations
1 Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, IN
2 Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, IN
3 Department of Electronics and Communication Engineering, S.R.Engineering College, Warangal, IN
4 Department of Computer Science and Engineering, Shadan College of Engineering, Hyderabad, IN
5 School of Electrical and Electronics, Sathyabama University, Chennai, IN
1 Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, IN
2 Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, IN
3 Department of Electronics and Communication Engineering, S.R.Engineering College, Warangal, IN
4 Department of Computer Science and Engineering, Shadan College of Engineering, Hyderabad, IN
5 School of Electrical and Electronics, Sathyabama University, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 4 (2019), Pagination: 1901-1906Abstract
The segmentation of retinal blood vessels and optical disc is the most vital and challenging task to investigate the rigorousness of the various retinal diseases such as branch retinal vein occlusion. There are lot of methods and algorithms are developed to address this issue i.e., for the precise segmentation of optical disc and blood vessels. However, every method has its own pros and cons. Retinal vein occlusion (RVO) happens due to the obstruction (blockage) of veins transporting blood with required nutrients and oxygen to the nerve cells in the eye’s retina. An obstruction in any one of the four smaller branch veins is referred to as a branch retinal vein occlusion (BRVO). It is one of the main retinal illnesses next only to diabetic retinopathy. Our proposed approach is a simple image processing based detection of optical disc and retinal blood vessels of branch retinal vein occluded fundus images.Keywords
Branch Retinal Vein Occlusion, Mathematical Morphology, Retinal Blood Vessel Segmentation, Optical Disc, Contrast Enhanced Adaptive Histogram Equalization, Median Filtering.References
- Rossant F, Badellino M, Chavillon A, Bloch I, and Paques M. A morphological approach for vessel segmentation in eye fundus images, with quantitative evaluation. J. Med. Imaging. Health. Inf. 1(2); 2011; 42–49.
- Soares J and Cree M. Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 25; 2006; 1214–1222.
- Zhang B, Zhang L, Zhang L, and Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40(1); 2010; 438–445.
- J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp. 501-509.
- Li H, Chutatape, O. Automated feature extraction in color retinal images by a model based approach.. IEEE Trans. Biomed. Eng. 51; 2004; 246–254.
- Jelinek HF, Cree MJ, Leandro JJG, Soares JVB, Cesar RM, and Luckie A, "Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy", JOSA A 24(5): 1448-1456, 2007.
- Luo, C. Opas, and Shankar M. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans. Biomed. Eng. 49(1); 2008; 168–172.
- Perfetti R, Ricci E, Casali D, et al., "Cellular neural networks with virtual template expansion for retinal vessel segmentation", IEEE Transactions on Circuits and Systems II 54(2): 141-145, 2007.
- Kalist V, Ganesan P, Sathish BS, and Jenitha JMM. Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space. Procedia Computer Science. 57; 2015; 49-56.
- Shaik KB, Ganesan P, Kalist V, and Sathish BS. Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space. Procedia Computer Science. 57; 2015; 41-48.
- Ganesan P and Shaik KB. HSV color space based segmentation of region of interest in satellite images. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). 2014; 101-105.doi: 10.1109/ICCICCT.2014.6992938
- Sajiv G and Ganesan P. Comparative Study of Possiblistic Fuzzy C-Means Clustering based Image Segmentation in RGB and CIELuv Color Space. International Journal of Pharmacy & Technology. 8(1); 2016; 10899-10909.
- Malay Bhushan, Niraj Bishwash, and kalist V. Wireless Power Transfer Platform for Smart Home Appliances. International Journal of Pharmacy & Technology. 8(3); 2016; .15669-15674.
- Sajiv G. Unsupervised Clustering of Satellite Images in CIELab Color Space using Spatial Information Incorporated FCM Clustering Method. International Journal of Applied Engineering Research. 10(20); 2015.
- Sathish BS, Ganesan P and Khamar Basha.Shaik. Color Image Segmentation based on Genetic Algorithm and Histogram Threshold. International Journal of Applied Engineering Research. 10(6); 2015; 123-127.
- Thakur M, Raj I and Ganesan P. The cooperative approach of genetic algorithm and neural network for the identification of vehicle License Plate number. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). 2015; 1-6.
- https://www.isi.uu.nl/Research/Databases/DRIVE/
- Ganesan P and B. S. Sathish. Automatic Detection of Optic Disc and Blood Vessel in Retinal Images using Morphological Operations and Ipachi Model. Research J. Pharm. and Tech. 10(8): August 2017; 2602-2607.
- Adam Hoover and Michael Goldbaum, “ Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels”, IEEE Transactions on Medical Imaging, Vol. 22, No. 8, August 2003, pp.951-957.
- Guillermo Ayala, Teresa León, and Victoria Zapater, “ Different Averages of a Fuzzy Set with an application to Vessel Segmentation”, IEEE Transactions on Fuzzy Systems, Vol. 13, No. 3, June 2005, pp.384-393.
- Ganesan P, M.Ganesh , L.M. I. Leo Joseph and V. Kalist, “ Central Retinal Vein Occlusion: An Approach for the Detection and Extraction of Retinal Blood Vessels”, J. Pharm. Sci. & Res. Vol. 10(1), 2018, 192-195.
- Meindert Niemeijer, Bram van Ginneken, Maria S. A. Suttorp-Schulten, and Michael D. Abràmoff, ” Automatic Detection of Red Lesions in Digital Color Fundus Photographs, IEEE Transactions on Medical Imaging, Vol. 24, No. 5, May 2005, pp.584-592.
- Tatijana Stoˇsic´ and Borko D. Stoˇsic´, “Multifractal Analysis of Human Retinal Vessels”, IEEE Transactions on Medical Imaging, Vol. 25, No. 8, August 2006. pp.1101-1108.
- Ganesan P,, “Detection and Segmentation of Retinal Blood Vessel in Digital RGB and CIELUV color space Fundus Images”, Research J. Pharm. and Tech. 11(6): 2018, 2326-2330.
- Ana Maria Mendonça, and Aurélio Campilho, ” Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction”, IEEE Transactions on Medical Imaging, Vol. 25, No. 9, September 2006, pp.1200-1213.
- A Simple Approach to Automated Brain Tumor Segmentation and Classification
Abstract Views :319 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, IN
2 Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, IN
3 Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, IN
1 Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, IN
2 Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, IN
3 Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 7 (2019), Pagination: 3564-3568Abstract
Brain tumor is the abnormal growth of superfluous cells in central nervous system or brain. It is fact that brain tumor is second most common of cancer death among young people. There are two key categories of brain tumor as cancerous and non cancerous. The cancerous brain tumor is called as Malignant. It spreads very quickly and difficult to remove. The non-cancerous tumor, called Benign, growth rate is very slow as compared to malignant one and easy to remove. The work proposes a simple but more efficient method to detect and segment the brain tumor from the MRI image. The proposed work based on the threshold segmentation for the segmentation of the brain tumor. The MRI image of the brain is taken and processed in such a way so that the tumor is extracted from the given MRI image and displays the segmented part of the image which contains the tumor. The otsu global threshold performs tumor segmentation and image area opening applies to remove the small components form the tumor portion. The gray level co-occurrence matrix and other image quality measures computes (extracts) the features from the segmented image. Support vector machine classifier is finally classifies the tumor, either benign or malignant, based on the extracted features.Keywords
Brain Tumor, Threshold, Principal Component Analysis, Discrete Wavelet Transform, Gray-Level Co-Occurrence Matrix, Support Vector Machine.References
- S. Pereira, A. Pinto, V. Alves and C. A. Silva, "Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240-1251, May 2016.
- S. Bauer, "A survey of MRI-based medical image analysis for brain tumor studies", Phys. Med. Biol., vol. 58, no. 13, pp. 97-129, 2013.
- Kalist V, Ganesan P, Sathish BS, and Jenitha JMM. Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space. Procedia Computer Science. 57; 2015; 49-56.
- J.J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, A. Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification", IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640, 2008.
- R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, A. Hammers, "Automatic anatomical brain MRI segmentation combining label propagation and decision fusion", Neuro Image, vol. 33, pp. 115-126, 2006.
- Shaik KB, Ganesan P, Kalist V, and Sathish BS. Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space. Procedia Computer Science. 57; 2015; 41-48.
- M. Huang, W. Yang, Y. Wu, J. Jiang, W. Chen and Q. Feng, "Brain Tumor Segmentation Based on Local Independent Projection-Based Classification," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 10, pp. 2633-2645, Oct. 2014.
- S. Bauer, L. P. Nolte, M. Reyes, "Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization", Proc. Med. Image Comput. Comput. Assist. Interv., pp. 354-361, 2011.
- Ganesan P and Shaik KB. HSV color space based segmentation of region of interest in satellite images. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). 2014; 101-105.doi: 10.1109/ICCICCT.2014.6992938
- Sajiv G and Ganesan P. Comparative Study of Possiblistic Fuzzy C-Means Clustering based Image Segmentation in RGB and CIELuv Color Space. International Journal of Pharmacy & Technology. 8(1); 2016; 10899-10909.
- Malay Bhushan, Niraj Bishwash, and kalist V. Wireless Power Transfer Platform for Smart Home Appliances. International Journal of Pharmacy & Technology. 8(3); 2016; .15669-15674.
- Sajiv G. Unsupervised Clustering of Satellite Images in CIELab Color Space using Spatial Information Incorporated FCM Clustering Method. International Journal of Applied Engineering Research. 10(20); 2015.
- Sathish BS, Ganesan P and Khamar Basha.Shaik. Color Image Segmentation based on Genetic Algorithm and Histogram Threshold. International Journal of Applied Engineering Research. 10(6); 2015; 123-127.
- Thakur M, Raj I and Ganesan P. The cooperative approach of genetic algorithm and neural network for the identification of vehicle License Plate number. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). 2015; 1-6.
- Ganesan P and B. S. Sathish. Automatic Detection of Optic Disc and Blood Vessel in Retinal Images using Morphological Operations and Ipachi Model. Research J. Pharm. and Tech. 10(8): August 2017; 2602-2607.
- Wulandari, R. Sigit and M. M. Bachtiar, "Brain Tumor Segmentation to Calculate Percentage Tumor Using MRI," 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia, 2018, pp. 292-296.
- Huang Meiyan, Wei Yang, Wu Yao, Jiang Jun, Chen Wufan, Qianjin Feng, "Brain Tumor Segmentation Based on Local Independent Projection-based Classification", IEEE Transactions on Biomedical Engineering, 2013.
- Ganesan P, M.Ganesh , L.M.I.Leo Joseph and V.Kalist, “ Central Retinal Vein Occlusion: An Approach for the Detection and Extraction of Retinal Blood Vessels”, J. Pharm. Sci. & Res. Vol. 10(1), 2018, 192-195.
- D. Bhattacharyya, T. H. Kim, "Brain tumor detection using MRI image analysis", Commun. Comput. Inform. Sci., vol. 151, pp. 307-314, 2011.
- C. L. Biji, D. Selvathi, A. Panicker, "Tumor detection in brain magnetic resonance images using modified thresholding techniques", Commun. Comput. Inform. Sic., vol. 4, pp. 300-308, 2011.
- Ganesan P,, “Detection and Segmentation of Retinal Blood Vessel in Digital RGB and CIELUV color space Fundus Images”, Research J. Pharm. and Tech. 11(6): 2018, 2326-2330.
- T. M. Hsieh, Y. M. Liu, C. C. Liao, F. Xiao, I. J. Chiang, J. M. Wong, "Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing", BMC Med. Informat. Decision Making, vol. 11, pp. 54, 2011.
- https://www.mathworks.com/help/matlab/