Uncertainty assessment of vessels width measurement from intensity profile model fitting in fundus images

Girard, Fantin; Kavalec, Conrad; Tahar, Houssem Ben; Cheriet, Foued

doi:10.17077/omia.1007

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the MESSIDOR dataset, this was not an issue as all the images are macula-centered and all have similar FOVs. The vessels map diameters were obtained using the method described in [17] and the optic discs were detected with the method described in [52].…”

Section: Application To Avr Measurement and Novel Global Avr Measurementioning

confidence: 99%

“…Matched filtering consists in convolving the image with a multiscale kernel that models the structure to be segmented. Vessels may be simply modeled by a Gaussian [8] or more complex distribution that accounts for light reflections or bifurcations [16,17]. The subsequent thresholding can be adaptive, like hysteresis thresholding [9,18,12], and be postprocessed with morphological operations [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint segmentation and classification of retinal arteries/veins from fundus images

Girard

Kavalec

Cheriet

2019

Artificial Intelligence in Medicine

Self Cite

View full text Add to dashboard Cite

Objective: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods: A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree.Results: The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion: The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance: The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.

show abstract

Section: Application To Avr Measurement and Novel Global Avr Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint segmentation and classification of retinal arteries/veins from fundus images

Girard

Kavalec

Cheriet

2019

Artificial Intelligence in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images

2018

View full text Add to dashboard Cite

BackgroundChanges in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task.MetholodogyA method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection.Results and conclusionsThe performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.

show abstract

Uncertainty assessment of vessels width measurement from intensity profile model fitting in fundus images

Cited by 2 publications

References 14 publications

Joint segmentation and classification of retinal arteries/veins from fundus images

Joint segmentation and classification of retinal arteries/veins from fundus images

Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images

Contact Info

Product

Resources

About