A Function for Quality Evaluation of Retinal Vessel Segmentations

Gegúndez-Arias, Manuel Emilio; Aquino, Arturo; Bravo, José Manuel; Marín, D.

doi:10.1109/tmi.2011.2167982

Cited by 78 publications

(47 citation statements)

References 42 publications

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“…All metrics described above are based on the pixel-to-pixel comparison between the segmented image and the GS, without considering that vessel pixels are part of a connected vascular structure with specific features, such as area and length. For this reason, the use of three additional metric functions is suggested in [132]:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

423

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: Evaluation Metricsmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

423

263

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and STARE as the evaluating data. Fig.…”

Section: Enhancement Resultsunclassified

“…The visualization of these blood vessels is important for disease diagnosis and improving the planning and navigation in interventional procedures [1][2][3][4]. For instance, retinal vessel images are widely used by ophthalmologists for the disease diagnosis such as diabetes, hypertension, cardiovascular disease and stroke.…”

Section: Introductionmentioning

confidence: 99%

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Chen

Shao

et al. 2016

Neurocomputing

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Blood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and SparseCoding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details.

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“…These metrics are specific to the vascular network and are insensitive to small tracing differences in grader segmentations. 41 Cohen's κ (Ref. 42) was computed as a metric of segmentation agreement.…”

Section: Metricsmentioning

confidence: 99%

Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

et al. 2013

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Abstract. The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.

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A Function for Quality Evaluation of Retinal Vessel Segmentations

Cited by 78 publications

References 42 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

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