Macular Edema Computer-Aided Evaluation in Ocular Vein Occlusions

Martínez-Costa, Lucía; Marco, P.; Ayala, Guillermo; Ves, Esther de; Domingo, Juan Carlos Ureta; Simó, Amelia

doi:10.1006/cbmr.1998.1487

Cited by 10 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While several investigators have utilized automated segmentation for automatic analysis of FA, [10][11][12][13][14][15][16][17][18][19][20][21] MA detection, [22][23][24][25][26][27] extraction of vessels, 15,18,19,28 and foveal avascular zone (FAZ) detection, 14,[29][30][31][32][33] relatively few algorithms have been focused on automated leakage detection or quantification. 10,[34][35][36][37][38][39] Martinez-Costa et al 35 have published a method for detection of macular angiographic leakage due to retinal vein occlusion. The foveal center is manually detected, and then images are aligned automatically.…”

mentioning

confidence: 99%

“…This algorithm analyzes only one early frame and one late frame, and along with the previously noted studies, uses rigid phase correction registration. All these noted methods either use rigid registration [35][36][37][38][39]41 (the shortcomings of which will be experimentally proven for our problem) or require manual inputs 35,37,38,41 (e.g., in the registration step or for fovea detection).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Rabbani

Allingham

Mettu

et al. 2015

Investigative Ophthalmology & Visual Science

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Rabbani

Allingham

Mettu

et al. 2015

Investigative Ophthalmology & Visual Science

View full text Add to dashboard Cite

show abstract

“…This method is designed to detect areas of leakage in a circular region centered at the fovea with a radius of 1500µm, and has a relatively low sensitivity of 0.69 on 24 images. Martinez-Costa et al [13] suggested that any pixels with statistically high increments in gray level along the FA sequence close to the foveal centre could be segmented as leakage, and applied this criterion to detect the leakage in the macua due to retinal vein occlusion. However, this method requires manual detection of the foveal center.…”

Section: A Leakage Detectionmentioning

confidence: 99%

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Zhao

Zheng

Liu

et al. 2017

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

show abstract

“…The Ophthalmic Imaging Research Group at Aberdeen University developed an automated method to detect macular leakage in fundus angiograms on a pixelby-pixel basis by this principle [11], [10], [3]. A similar approach is seen in the work of Martínez-Costa et al [9]. However, only using a pair of frames (or three pairs in the case of majority voting [10], [3]) excludes a considerable amount of temporal information.…”

Section: Related Workmentioning

confidence: 99%

Contextual detection of diabetic pathology in wide-field retinal angiograms

Buchanan

Trucco

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

We report a novel algorithm to locate vascular leakage and ischemia in retinal angiographic image sequences leveraging contextual knowledge of co-occurring pathologies. The key contributions are the use of spatio-temporal features exploiting the evolution of intensity levels over the sequence and contextual knowledge to detect ischemia. The specific nature of these diseased regions is determined using an AdaBoost learning algorithm. Training was performed with a varied set of 16 ground-truth image sequences, and testing on unseen images. The images used were acquired with an Optos ultrawide-field scanning laser ophthalmoscope. Evaluation against manual annotations demonstrates successful location of 93% of leakage regions and 70% of ischemic regions.

show abstract

Macular Edema Computer-Aided Evaluation in Ocular Vein Occlusions

Cited by 10 publications

References 16 publications

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Contextual detection of diabetic pathology in wide-field retinal angiograms

Contact Info

Product

Resources

About