An active contour model using matched filter and Hessian matrix for retinal vessels segmentation

Medical image analysis, especially of the retina, plays an important role in diagnostic decision support tools. The properties of retinal blood vessels are used for disease diagnoses such as diabetes, glaucoma, and hypertension. There are some challenges in the utilization of retinal blood vessel patterns such as low contrast and intensity inhomogeneities. Thus, an automatic algorithm for vessel extraction is required. Active contour is a strong method for edge extraction. However, it cannot extract thin vessels and ridges very well. In this research, we propose an improved active contour method that uses discrete wavelet transform for energy minimization to solve this problem. The minimization formula terminates segmentation into two regions, foreground and background. We found out that foreground pixels are more important than background. Therefore, we change the minimization formulation in such a way that gives more weight to the foreground. The contour edge has orientation in each region. The wavelet terms in the minimization formula help to detect edge direction in horizontal, vertical, and diagonal orientations. Since bright and dark lesions do not have direction, these terms can decrease false vessel detection. The second part of the innovation is called the optimization process, which works instead of reinitialization. Sometimes, the evolution in the iterated process destroys the stability of the evolution. To prevent the destruction of the stability of evolution, we used an optimization process formula whose task is to keep contour on the edges of the image. The performance of the proposed algorithms is compared and analyzed on five databases. The values achieved are 94.3%, 73.36%, and 97.41% for accuracy, sensitivity, and specificity, respectively, on the DRIVE dataset, and the proposed algorithm is comparable to the state-of-the-art approaches.

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[1] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR et al. Blood vessel segmentation methodologies in retinal images – A survey. Computer Methods and Programs in Biomedicine 2012; 108 (1): 407-433. doi: 10.1016/j.cmpb.2012.03.009
[2] Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Computing Surveys (CSUR) 2004; 36 (2): 81-121. doi: 10.1145/1031120.1031121
[3] Wang Y, Ji G, Lin P, Trucco E. Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognition 2013; 46 (8): 2117-2133. doi: 10.1016/j.patcog.2012.12.014
[4] Nguyen UT, Bhuiyan A, Park LA, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern recognition 2013; 46 (3): 703-715. doi: 10.1016/j.patcog.2012.08.009
[5] Dash S, Senapati MR. Enhancing detection of retinal blood vessels by combined approach of DWT, Tyler Coye and Gamma correction. Biomedical Signal Processing and Control 2020; 57: 101740. doi: 10.1016/j.bspc.2019.101740
[6] Gao J, Chen G, Lin W. An effective retinal blood vessel segmentation by using automatic random walks based on centerline extraction. BioMed Research International 2020; 2020: 7352129. doi: 10.1155/2020/7352129
[7] Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on medical imaging 1989; 8 (3): 263-269. doi: 10.1109/42.34715
[8] Zolfagharnasab H, Naghsh-Nilchi AR. Cauchy based matched filter for retinal vessels detection. Journal of Medical Signals and Sensors 2014; 4(1): 1.
[9] Singh NP, Srivastava R. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Computer Methods and Programs in Biomedicine 2016; 129: 40-50. doi: 10.1016/j.cmpb.2016.03.001
[10] Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine 2010; 40 (4): 438-445. doi: 10.1016/j.compbiomed.2010.02.008
[11] Melinscak M, Prentasic P, Loncaric S. Retinal Vessel Segmentation using Deep Neural Networks. in VISAPP 2015; doi: 10.5220/0005313005770582
[12] Shin SY, Lee S, Yun ID, Lee KM. Deep vessel segmentation by learning graphical connectivity. Medical Image Analysis 2019; 58: 101556. doi: 10.1016/j.media.2019.101556
[13] Li Q, Feng B, Xie L, Liang P, Zhang H et al. A cross-modality learning approach for vessel segmentation in retinal images. IEEE transactions on medical imaging 2015; 35 (1): 109-118. doi: 10.1109/TMI.2015.2457891
[14] Lázár I, Hajdu A. Segmentation of retinal vessels by means of directional response vector similarity and region growing. Computers in biology and medicine 2015; 66 (11): 209-221. doi: 10.1016/j.compbiomed.2015.09.008
[15] Zhao YQ, Wang XH, Wang XF, Shih FY. Retinal vessels segmentation based on level set and region growing. Pattern Recognition 2014; 47 (7): 2437-2446. doi: 10.1016/j.patcog.2014.01.006
[16] Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing 2011; 20 (7): 2007-2016. doi: 10.1109/TIP.2011.2146190
[17] Al-Diri B, Hunter A, Steel D. An Active Contour Model for Segmenting and Measuring Retinal Vessels. IEEE Transactions on Medical Imaging 2009; 28 (9): 1488-1497. doi: 10.1109/TMI.2009.2017941
[18] Zhao Y, Rada L, Chen K, Harding SP, Zheng Y. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Transactions on Medical Imaging 2015; 34 (9): 1797-1807. doi: 10.1109/TMI.2015.2409024
[19] Chan TF, Vese LA. Active contours without edges. IEEE Transactions on image processing 2001; 10 (2): 266-277. doi: 10.1109/83.902291
[20] Heneghan C, Flynn J, O’Keefe M, Cahill M. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Medical image analysis 2002; 6 (4): 407-429. doi: 10.1016/S1361- 8415(02)00058-0
[21] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on pure and applied mathematics 1989; 42 (5): 577-685. doi:10.1002/cpa.3160420503
[22] Crandall R. Image segmentation using the Chan–Vese algorithm. in Project report from ECE 2009.
[23] Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989; 11 (7): 674-693. doi: 10.1515/9781400827268.494
[24] Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering, in Medical Image Computing and Computer-Assisted Intervention — MICCAI’98: First International Conference Cambridge, MA, USA, October 11–13, 1998 Proceedings. Wells W.M, Colchester A, Delp S. Editors. Springer Berlin Heidelberg: Berlin, Heidelberg, 1998, pp. 130-137. doi: 10.1007/BFb0056195
[25] Otsu N. A threshold selection method from gray-level histograms. Automatica 1975; 11 (285-296): 23-27.
[26] Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J et al. Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Processing 2013; 7 (4): 373-383. doi:10.1049/iet-ipr.2012.0455
[27] Fraz MM, Basit A, Barman S. Application of morphological bit planes in retinal blood vessel extraction. Journal of digital imaging 2013; 26 (2): 274-286. doi: 10.1016/j.cmpb.2012.03.009
[28] BahadarKhan K, A Khaliq A, Shahid M. A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding. PLOS ONE 2016; 11 (7): e0158996. doi: 10.1371/journal.pone.0158996
[29] Singh NP, Srivastava R. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Computer Methods and Programs in Biomedicine 2016; 129 (6): 40-50. doi: 10.1016/j.cmpb.2016.03.001
[30] Roy S, Mitra A, Roy S, Setua SK. Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution. Multimedia Tools and Applications 2019; 78 (24): 34839-34865. doi: 10.1007/s11042-019- 08111-0
[31] Shukla AK, Pandey RK, Pachori RB. A fractional filter based efficient algorithm for retinal blood vessel segmentation. Biomedical Signal Processing and Control 2020; 59: 101883. doi: 10.1016/j.bspc.2020.101883
[32] Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on medical Imaging 2006; 25 (9): 1214-1222. doi: 10.1109/TMI.2006.879967