Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile
Authors: 
Sonali Dash, Sahil Verma, Kavita, SeongKi Kim, Jana Shafi, Muhammad Fazal Ijaz Academic Editor: Sudipta RoyAuthors Info & Claims
Sonali Dash, Sahil Verma, Kavita, SeongKi Kim, Jana Shafi, Muhammad Fazal Ijaz Academic Editor: Sudipta RoyAuthors Info & ClaimsAbstract
It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.
References
[1]
M. D. Abramoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Reviews in Biomedical Engineering, vol. 3, pp. 169–208, 2010.
[2]
G. Liew and J. J. Wang, “Retinal vascular signs: a window to the heart?” Revista Española de Cardiología, vol. 64, no. 6, pp. 515–521, 2011.
[3]
K. Narasimhan, V. C. Neha, and K. Vijayarekha, “Hypertensive retinopathy diagnosis from fundus images by estimation of Avr,” Procedia Engineering, vol. 38, pp. 980–993, 2012.
[4]
B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Transactions on Medical Imaging, vol. 28, no. 9, pp. 1488–1497, 2009.
[5]
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269, 1989.
[6]
S. Roy, T. D. Whitehead, S. Li, F. O. Ademuyiwa, R. L. Wahl, F. Dehdashti, and K. I. Shoghi, “Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 49, no. 2, pp. 550–562, Jan 2022.
[7]
A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000.
[8]
J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501–509, 2004.
[9]
S. Roy and K. I. Shoghi, “Computer-aided tumor segmentation from T2-weighted MR images of patients derived tumor xenografts,” in Image Analysis and Recognition. ICAR 2019, vol. 11663, F. Karray, A. Campilho, and A. Yu, Eds., Springer, Cham, 2019.
[10]
X. Xiaoyi Jiang and D. Mojon, “Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 131–137, 2003.
[11]
G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, “Trainable COSFIRE filters for vessel delineation with application to retinal images,” Medical Image Analysis, vol. 19, no. 1, pp. 46–57, 2015.
[12]
T. Mapayi, S. Viriri, and J.-R. Tapamo, “Comparative study of retinal vessel segmentation based on global thresholding techniques,” Computational and Mathematical Methods in Medicine, vol. 2015, 15 pages, Feb 2015.
[13]
N. Strisciuglio, G. Azzopardi, M. Vento, and N. Petkov, “Multiscale blood vessel delineation using B-COSFIRE filters,” in Proceedings of the International Conference on Computer Analysis of Images and Patterns, pp. 300–312, Valetta, Malta, September 2015.
[14]
S. Roy, T. D. Whitehead, J. D. Quirk, A. Salter, F. O. Ademuyiwa, S. Li, H. An, and K. I. Shoghi, “Optimal co-clinical radiomics: sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging,” EBioMedicine, vol. 59, Sep 2020.
[15]
Y. Rajput, R. manza, M. Patwari, and N. Deshpande, M. Jalgaon, ““Retinal blood vessels extraction using 2D median filter,” in Proceedings of the Third National Conference on Advances in Computing, pp. 58–59, Chennai, Tamil Nadu, India, March 2013.
[16]
Maison, T. Lestari, and A. Luthfi, “Retinal blood vessel segmentation using Gaussian filter,” Journal of Physics: Conference Series, vol. 1376, no. 1, pp. 012023–012028, 2019.
[17]
M. Malarvel and S. R. Nayak, “Edge and region segmentation in high-resolution aerial images using improved kernel density estimation: a hybrid approach,” Journal of Intelligent and Fuzzy Systems, vol. 39, no. 1, pp. 543–560, 1 Jan.2020.
[18]
J. Kaur and H. P. Sinha, “Automated detection of retinal blood vessels in diabetic retinopathy using Gabor filter,” Int. J of Comp Sc and Net., vol. 4, pp. 109–116, 2012.
[19]
X.-R. Bao, X. Ge, L.-H. She, and S. Zhang, “Segmentation of retinal blood vessels based on cake filter,” BioMed Research International, vol. 2015, 11 pages, 2015.
[20]
S. Chatterjee, R. K. Dutta, D. Ganguly, K. Chatterjee, and S. Roy, U. Tiwary and S. Chaudhury, ““Bengali Handwritten Character Classification Using Transfer Learning on Deep Convolutional Network,” in Intelligent Human Computer Interaction. IHCI 2019, vol. 11886, Springer, Cham, 2019.
[21]
B. Kochner, D. Schuhmann, M. Michaelis, G. Mann, and K.-H. Englmeier, “Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model-based image analysis,” in Proceedings of the Medical Imaging 1998: Image Processing, pp. 755–761, San Diego, CA, United States, 24 June 1998.
[22]
F. Sabaz and U. Atila, “ROI detection and vessel segmentation in retinal image,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-4/W6, pp. 85–89, 2017.
[23]
S. Dash and G. Sahu, “Retinal blood vessel segmentation by employing various upgraded median filters,” in Proceedings of the IEEE International Conference on Intelligent Systems and Green Technology, pp. 35–39, Visakhapatnam, AP, June 2019.
[24]
C. Yao and H.-j. Chen, “Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-otsu algorithm,” Journal of Central South University of Technology, vol. 16, no. 4, pp. 640–646, 2009.
[25]
S. Roy, A. Mitra, S. Roy, and S. K. Setua, “Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution,” Multimedia Tools and Applications, vol. 78, no. 24, pp. 34839–34865, Dec 2019.
[26]
M. G. Cinsdikici and D. Aydın, “Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm,” Computer Methods and Programs in Biomedicine, vol. 96, no. 2, pp. 85–95, 2009.
[27]
B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of Gaussian,” Computers in Biology and Medicine, vol. 40, no. 4, pp. 438–445, 2010.
[28]
S. Roy, D. Bhattacharyya, S. K. Bandyopadhyay, and T.-H. Kim, “An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images,” IETE Journal of Research, vol. 63, no. 6, pp. 769–783, Jun 2017.
[29]
M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Computers in Biology and Medicine, vol. 37, no. 2, pp. 262–267, 2007.
[30]
M. Al-Rawi and H. Karajeh, “Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images,” Computer Methods and Programs in Biomedicine, vol. 87, no. 3, pp. 248–253, 2007.
[31]
K. S. Sreejini and V. K. Govindan, “Improved multiscale matched filter for retina vessel segmentation using PSO algorithm,” Egyptian Informatics Journal, vol. 16, no. 3, pp. 253–260, 2015.
[32]
H. P. Chaudhari, A. D. Rahulkar, and C. Y. Patil, “Segmentation of retinal vessels by the use of gabor wavelet and linear mean squared error classifier,” Int. .J Emerg. Res. Tech., vol. 2, pp. 119–125, 2014.
[33]
J. V. B. Soares, J. J. G. Leandro, R. M. Cesar Jr, H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1214–1222, 2006.
[34]
S. Shabbir, A. Tariq, and M. U. Akram, “A Comparison and evaluation of computerized methods for blood vessel enhancement and segmentation in retinal images,” International Journal of Future Computer and Communication, vol. 2, pp. 600–603, 2013.
[35]
H. Aguirre-Ramos, J. G. Avina-Cervantes, I. Cruz-Aceves, J. Ruiz-Pinales, and S. Ledesma, “Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization,” Applied Mathematics and Computation, vol. 339, pp. 568–587, 2018.
[36]
Z. Yavuz and C. Köse, “Blood vessel extraction in color retinal fundus images with enhancement filtering and unsupervised classification,” Journal of Healthcare Engineering, vol. 2017, 12 pages, 2017.
[37]
F. Farokhian, C. Yang, H. Demirel, S. Wu, and I. Beheshti, “Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation,” Biocybernetics and Biomedical Engineering, vol. 37, no. 1, pp. 246–254, 2017.
[38]
R. Sundaram, R. Ks, P. Jayaraman, and V. B, “Extraction of blood vessels in fundus images of retina through hybrid segmentation approach,” Mathematics, vol. 7, no. 2, pp. 169–217, 2019.
[39]
S. Dash, S. Verma, Kavita, M. S. Khan, M. Wozniak, J. Shafi, and M. F. Ijaz, “A hybrid method to enhance thick and thin vessels for blood vessel segmentation,” Diagnostics, vol. 11, no. 11, p. 2017, 2021.
[40]
Y. Dagli, S. Choksi, and S. Roy, “Prediction of two year survival among patients of non-small cell lung cancer,” in Computer Aided Intervention and Diagnostics in Clinical and Medical Images, vol. 31, J. Peter, S. Fernandes, C. Eduardo Thomaz, and S. Viriri, Eds., pp. 169–177, Springer, Cham, Switzerland, 2019.
[41]
D. Primitivo, R. Alma, C. Erik, V. Arturo, C. Edgar, P.-C. Marco, and Z. Daniel, “A hybrid method for blood vessel segmentation in images,” Biocybernetics and Biomedical Engineering, vol. 39, no. 3, pp. 814–824, 2019.
[42]
M. Hashemzadeh and B. Adlpour Azar, “Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods,” Artificial Intelligence in Medicine, vol. 95, pp. 1–15, 2019.
[43]
V. Anand, S. Gupta, D. Koundal, S. R. Nayak, P. Barsocchi, and A. K. Bhoi, “Modified U-net architecture for segmentation of skin lesion,” Sensors, vol. 22, no. 3, p. 867, Jan 2022.
[44]
A. Khawaja, T. M. Khan, K. Naveed, S. S. Naqvi, N. U. Rehman, and S. Junaid Nawaz, “An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser,” IEEE Access, vol. 7, pp. 164344–164361, 2019.
[45]
B. Wang, S. Wang, S. Qiu, W. Wei, H. Wang, and H. He, “CSU-net: a Context spatial U-net for accurate blood vessel segmentation in fundus images,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 4, pp. 1128–1138, 2021.
[46]
C. Chen, J. H. Chuah, R. Ali, and Y. Wang, “Retinal vessel segmentation using deep learning: a review,” IEEE Access, vol. 9, pp. 111985–112004, 2021.
[47]
H. Kriplani, B. Patel, and S. Roy, “Prediction of chronic kidney diseases using deep artificial neural network technique,” in Computer Aided Intervention and Diagnostics in Clinical and Medical Images, vol. 31, J. Peter, S. Fernandes, C. Eduardo Thomaz, and S. Viriri, Eds., pp. 179–187, Springer, Cham, 2019.
[48]
P. N. Srinivasu, J. G. SivaSai, M. F. Ijaz, A. K. Bhoi, W. Kim, and J. J. Kang, “Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM,” Sensors, vol. 21, no. 8, p. 2852, 2021.
[49]
M. F. Ijaz, M. Attique, and Y. Son, “Data-driven cervical cancer prediction model with outlier detection and over-sampling methods,” Sensors, vol. 20, no. 10, p. 2809, 2020.
[50]
M. Malarvel and S. R. Nayak, “Region grow using fuzzy automated seed selection for weld defect segmentation in x-radiography image,” in Proceedings of the International Conference on Artificial Intelligence in Manufacturing & Renewable Energy, SSRN, Elsevier, Bhubaneswar, India, 2019.
[51]
P. Naga Srinivasu, S. Ahmed, A. Alhumam, A. Bhoi Kumar, and M. Fazal Ijaz, “An AW-HARIS based automated segmentation of human liver using CT images,” Computers, Materials & Continua, vol. 69, no. 3, pp. 3303–3319, 2021.
[52]
A. Vulli, P. N. Srinivasu, M. S. K. Sashank, J. Shafi, J. Choi, and M. F. Ijaz, “Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy,” Sensors, vol. 22, no. 8, p. 2988, 2022.
[53]
I. Rizwan I Haque and J. Neubert, “Deep learning approaches to biomedical image segmentation,” Informatics in Medicine Unlocked, vol. 18, 2020.
[54]
M. Sood, S. Verma, V. K. Panchal, and fnm Kavita, “Optimal path planning using swarm intelligence based hybrid techniques,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 9, pp. 3717–3727, 2019.
[55]
S.-C. Huang, F.-C. Cheng, and Y.-S. Chiu, “Efficient contrast enhancement using adaptive Gamma correction with weighting distribution,” IEEE Transactions on Image Processing, vol. 22, no. 3, pp. 1032–1041, 2013.
[56]
S. Rani, D. Koundal, fnm Kavita, M. F. Ijaz, M. Elhoseny, and M. I. Alghamdi, “An optimized framework for WSN routing in the Context of industry 4.0,” Sensors, vol. 21, no. 19, p. 6474, 2021.
[57]
S. Dash, U. R. Jena, and M. R. Senapati, “Homomorphic normalization-based descriptors for texture classification,” Arabian Journal for Science and Engineering, vol. 43, no. 8, pp. 4303–4313, 2018.
[58]
L. Gaur, G. Singh, A. Solanki, N. Z. Jhanjhi, U. Bhatia, S. Sharma, S. Verma, Kavita, N. Petrović, M. F. Ijaz, and W. Kim, “Disposition of youth in predicting sustainable development goals using the neuro-fuzzy and random forest algorithms,” Hum. Cent. Comput. and Inf. Sci., vol. 11, pp. 1–19, 2021.
[59]
S. Agrawal, R. Panda, P. K. Mishro, and A. Abraham, “A novel joint histogram equalization based image contrast enhancement,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1172–1182, 2022.
[60]
M. Kaur, S. Verma, and Kavita, “Flying ad-hoc network (FANET): challenges and routing protocols,” Journal of Computational and Theoretical Nanoscience, vol. 17, no. 6, pp. 2575–2581, 2020.
[61]
S. C. F. Lin, C. Y. Wong, G. Jiang, M. A. Rahman, T. R. Ren, N. Kwok, H. Shi, Y.-H. Yu, and T. Wu, “Intensity and edge based adaptive unsharp masking filter for color image enhancement,” Optik, vol. 127, no. 1, pp. 407–414, 2016.
[62]
T. Sharma, S. Verma, and Kavita, “Prediction of heart disease using Cleveland dataset: a machine learning approach,” Int. J. Rec. Res. Asp., vol. 4, pp. 17–21, 2017.
[63]
N. Kwok and H. Shi, “Design of unsharp masking filter kernel and gain using Particle Swarm Optimization,” in Proceedings of the International Congress on Image and Signal Processing, pp. 217–222, Dalian, China, October 2014.
[64]
G. Ghosh, fnm Kavita, D. Anand, S. Verma, D. B. Rawat, J. Shafi, Z. Marszałek, and M. Woźniak, “Secure surveillance systems using partial-regeneration-based non-dominated optimization and 5D-chaotic map,” Symmetry, vol. 13, no. 8, p. 1447, 2021.
[65]
D. Hasler and S. E. Suesstrunk, “Measuring colorfulness in natural images,” SPIE Proceedings, vol. 5007, pp. 87–95, 2003.
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