A hybrid method for blood vessel segmentation in images

Diaz, Parris; Rodríguez, Alma; Cuevas, Erik; Valdivia, Arturo; Chavolla, Edgar; Pérez‐Cisneros, Marco; Zaldívar, Daniel

doi:10.1016/j.bbe.2019.06.009

Cited by 21 publications

(11 citation statements)

References 52 publications

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“…Comparisons were made using the DRIVE and STARE datasets. Table 5 shows that Primitivo et al [22] has relatively the same performance as the research proposed when using the DRIVE dataset, but is lower for the STARE dataset. In this study, the segmentation model used is divided into two, namely blood vessel segmentation and optic disc removal.…”

Section: Methodsmentioning

confidence: 76%

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto

2019

IJFIS

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Blood vessels in the retina of the eye are one important sign when making a diagnosis of hypertensive retinopathy. On the retina can be known several signs including tortuosity and arteriovenous ratio. Blood vessels mixed with a number of objects in the retina, the segmentation of blood vessels becomes a very interesting challenge because they have to separate blood vessels from a number of objects. This study aims to segmentation blood vessels using the main method of self-organizing maps artificial neural networks (SOM-ANN). The proposed segmentation method is divided into three stages, namely preprocessing, segmentation, and performance analysis. The preprocessing step is to improve image quality using the contrast-limited adaptive histogram equalization (CLAHE), median filter, and morphology. The segmentation stage uses the SOM-ANN algorithm combined with the mean or median thresholding. The performance parameters which are measured consist of sensitivity, specificity, and area under the curve (AUC). The test results using the dataset STARE and DRIVE show that the median thresholding is able to provide the best AUC performance compared to the mean thresholding. The proposed segmentation model is able to provide performance in the excellent category, with AUC values of 90.55% for the STARE dataset and 90.35% for the DRIVE.

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Section: Methodsmentioning

confidence: 76%

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto

2019

IJFIS

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“…Sazak et al [25] have recommended a vessel enhancement and extraction method using multiscale bowler-hat transform. Primitivo et al [26] have suggested a hybrid model by combining Lateral Inhibition and Differential Evolution for retinal vessel segmentation. Shah et al [27] have recommended a model of Gabor wavelet and line detector for vessel extraction.…”

Section: Related Workmentioning

confidence: 99%

“…, Azzopardi et al[15], Roychowdhury et al[16], Roychowdhury et al[17], Imani et al[18], Aslani et al[14], Panda et al[19], Tan et al[20], Rodrigues et al[21], Farokhian et al[22], Jiang et al[23], Sazak et al[25], Primitivo et al[26], Shah et al[27], and Dash et al[28] on DRIVE database.…”

mentioning

confidence: 99%

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Dash¹,

Verma²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

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Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.

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“…A new improved curvelet transform technique is suggested to detect thick and thin blood vessels for extraction [ 40 ]. A hybrid method by combining two different existing techniques such as lateral inhibition and differential evolution is used for vessel segmentation [ 41 ]. Existing supervised and unsupervised machine learning techniques are utilized for vessel segmentation by employing image features [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…e average results from DRIVE and CHASE_DB1 data sets were compared with some other approaches. , and specificity for models presented in Cinsdikici and Aydın[26], Zhang et al[27], Rawi et al[29], Rawi and Karajeh[30], Sreejini and Govindan[31], Chaudhari et al[32], Soares et al[33], Shabbir et al[34], Aguirre-Ramos et al[35], Yavuz and Kose[36], Farokhian et al[37], Sundaram et al[38], Dash et al[36], Primitivo et al[41],…”

mentioning

confidence: 99%

Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile

Dash

Verma

Kavita

et al. 2022

Computational Intelligence and Neuroscience

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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.

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A hybrid method for blood vessel segmentation in images

Cited by 21 publications

References 52 publications

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile

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