Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Asl, Masoud Elhami; Koohbanani, Navid Alemi; Frangi, Alejandro F.; Gooya, Ali

doi:10.1117/1.jmi.4.3.034006

Cited by 17 publications

(11 citation statements)

References 50 publications

(45 reference statements)

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“…Analysis of tubular structures for biological applications plays crucial roles in diagnosing several pathologies such as cardiovascular disorders, diabetes, spinal stenosis, central retinal vein analysis, and hypertension [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. All of these applications would benefit from accurate and fast dimensioning for quicker diagnosis [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Diameter Estimation of Fallopian Tubes Using Visual Sensing

2021

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Calculating an accurate diameter of arbitrary vessel-like shapes from 2D images is of great use in various applications within medical and biomedical fields. Understanding the changes in morphological dimensioning of the biological vessels provides a better understanding of their properties and functionality. Estimating the diameter of the tubes is very challenging as the dimensions change continuously along its length. This paper describes a novel algorithm that estimates the diameter of biological tubes with a continuously changing cross-section. The algorithm, evaluated using various controlled images, provides an automated diameter estimation with higher and better accuracy than manual measurements and provides precise information about the diametrical changes along the tube. It is demonstrated that the automated algorithm provides more accurate results in a much shorter time. This methodology has the potential to speed up diagnostic procedures in a wide range of medical fields.

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Section: Introductionmentioning

confidence: 99%

“…Many studies model the vessels’ tortuosity by polynomial fitting techniques [ 1 , 31 , 32 ]. These algorithms increase the error in centreline estimation depending on the vessel’s complexity, reducing accuracy if used for diameter measurement.…”

Section: Introductionmentioning

confidence: 99%

Diameter Estimation of Fallopian Tubes Using Visual Sensing

2021

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“…Important works have been developed in the retina [7] and coronary arteries [8], but a thorough search of relevant literature revealed that no work has been done so far in the automatic identification of perforators for microsurgery.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of perforators for microsurgical reconstruction

et al. 2020

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a b s t r a c tThe deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction.Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction.In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task.Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P ¼ 6e-4) but better for the remaining ones (P ¼ 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P ¼ 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2e3 mm do not have impact in the dissection step.Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/ case).The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story.

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“…Unsupervised methods on the other hand does not need to have a prior training session, but use rule-based methods to identify vessel pixels against background pixels. Some of the available techniques include kernel-based method [3]- [5], vessel tracking method [6], [7], mathematical morphologybased method [8], [9], multi-scale method [10]- [12], machine learning method [13]- [16] and model-based method [17], [18]. B-COSFIRE, stands for Bar-selective Combination of Shifted Filter Responses, is an efficient method to segment RBV from digital fundus images [19].…”

Section: Introductionmentioning

confidence: 99%

Retinal blood vessel segmentation from retinal image using B-COSFIRE and adaptive thresholding

Ali¹,

Zaki²,

Hussain³

2019

IJEECS

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<span>Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.</span>

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Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Cited by 17 publications

References 50 publications

Diameter Estimation of Fallopian Tubes Using Visual Sensing

Diameter Estimation of Fallopian Tubes Using Visual Sensing

Automatic detection of perforators for microsurgical reconstruction

Retinal blood vessel segmentation from retinal image using B-COSFIRE and adaptive thresholding

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