Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion

Prentašić, Pavle; Lončarić, Sven

doi:10.1016/j.cmpb.2016.09.018

Cited by 138 publications

(69 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The retinal images are cut into patches of size 25x25 with three color channels which were labeled in two groups: (i) Exudates and (ii) Non-exudates. The work described by [11] proposes to combine the output of optic disc and vessel detection with the output of the convolutional neural network in order to detect exudates. The images used for training are divided into sub-images with 65*65 pixels.…”

Section: Dl-based Methods Vs Data Input Managementmentioning

confidence: 99%

“…Grassmann et al [6] method consists of normalizing the color balance as well as local illumination of each fundus image by using a Gaussian filtering to subtract the local average color. In [11], the original images are converted from RGB to Hue Saturation Intensity color space, and then denoised using median filter. Thereafter, CLAHE method is applied to enhance contrast.…”

Section: Preprocessing For Fundus Image Enhancementmentioning

confidence: 99%

“…Intel Core-i7 5820K, 64GB RAM GeForce GTX 770 GPU [9] Keras API for TensorFlow library --- [11] Caffe framework Tesla K20C graphics card [12] MATLAB Intel Xeon CPU E3-1225, 3.3GHz, 16GB RAM GPU [14] MATLAB CPU: 2.3 GHz, 4Gb RAM [1] Tensorflow library --- [15] MATLAB Intel Xeon 2.20 GHz (E5-2650 v4), 512GB RAM [16] Keras API for TensorFlow library Theano backend…”

Section: Hardware and Software Environmentsmentioning

confidence: 99%

See 2 more Smart Citations

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Elloumi

Akil

Boudegga

2019

Real-Time Image Processing and Deep Learning 2019

View full text Add to dashboard Cite

Ocular pathology detection from fundus images presents an important challenge on health care. In fact, each pathology has different severity stages that may be deduced by verifying the existence of specific lesions. Each lesion is characterized by morphological features. Moreover, several lesions of different pathologies have similar features. We note that patient may be affected simultaneously by several pathologies. Consequently, the ocular pathology detection presents a multiclass classification with a complex resolution principle. Several detection methods of ocular pathologies from fundus images have been proposed. The methods based on deep learning are distinguished by higher performance detection, due to their capability to configure the network with respect to the detection objective. This work proposes a survey of ocular pathology detection methods based on deep learning. First, we study the existing methods either for lesion segmentation or pathology classification. Afterwards, we extract the principle steps of processing and we analyze the proposed neural network structures. Subsequently, we identify the hardware and software environment required to employ the deep learning architecture. Thereafter, we investigate about the experimentation principles involved to evaluate the methods and the databases used either for training and testing phases. The detection performance ratios and execution times are also reported and discussed.

show abstract

Section: Dl-based Methods Vs Data Input Managementmentioning

confidence: 99%

Section: Preprocessing For Fundus Image Enhancementmentioning

confidence: 99%

Section: Hardware and Software Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Elloumi

Akil

Boudegga

2019

Real-Time Image Processing and Deep Learning 2019

View full text Add to dashboard Cite

show abstract

“…Finally, the resultant intensity image was thresholded to obtain the exudates, achieving a sensitivity and specificity of 86.2% and 85%, respectively, over the DIARETDB1 dataset. Prentasic and Loncaric (2016) detected the exudates using deep convolutional neural networks. First, the anatomical structures of the eye fundus were detected.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

Ghalwash¹,

Youssif²,

Allam³

2017

Journal of Computer Science

View full text Add to dashboard Cite

This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised kmeans algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fullyautomated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

show abstract

“…Different attempts have been performed in the literature in this sense. For example, in (Prentašić and Lončarić, 2016) a homemade convolutional neural network calculates the pixel probability of belonging to exudate or non-exudate classes. The authors propose a CNN architecture consisting of four convolutional layers and four max-pooling layers.…”

Section: Introductionmentioning

confidence: 99%

Fundus image analysis for automatic screening of ophthalmic pathologies.

Granero¹

View full text Add to dashboard Cite

AcknowledgmentsEste documento es el resultado de cuatro años de esfuerzo y dedicación que han propiciado mi evolución tanto a nivel profesional como personal. El principal motivo de dicho desarrollo han sido todas esas personas que han formado parte de este camino o que de alguna forma se han cruzado en el mismo. A todos ellos quiero dedicarles unas palabras de agradecimiento.En primer lugar, quiero agradecer a Valery, ella es la responsable de que yo esté ahora mismo escribiendo estas líneas. Me brindó la oportunidad de mi vida hace ya más de cinco años y me ha ido formando tanto en la vertiente investigadora como docente de la misma forma que una madre educa e inculca los valores esenciales a su hijo. Gracias por todo Valery, juntos cerramos una etapa clave en mi formación y juntos abrimos una nueva cargada de ilusión. Espero poder estar siempre a tu lado.También quiero agradecer todo el apoyo, energía y momentos divertidos que me han brindado mis compañeros de laboratorio. Gracias Sandra por todo lo que me has enseñado desde el primer momento en el que llegué hasta el día de hoy, no sé qué haría yo sin ti en esa sala. Gracias Fer, Félix, Andrés, Reinier, Ángel, Gabri, Fran P., Rober, Jorge y Adri por todos los consejos aportados y por amenizar como sólo vosotros sabéis mi día a día. También quiero recordar y agradecer a los que decidieron emprender otro rumbo: Javi, Conchi, Pablo, Eliseo, Fran M. y Miguel.De igual forma quiero mostrar mi gratitud hacia varias personas que han colaborado de una u otra forma en este trabajo. A Jesús Angulo, Débora Gil y Ramón Baldrich por su supervisión del trabajo desarrollado en las estancias llevadas a cabo durante esta etapa. A Kjersti Engan por aportar sus conocimientos sobre Dictionary Learning & Sparse Representation que han culminado en diversas publicaciones. A Rafael Molina por permitirme adentrarme en el fascinante mundo de los Procesos Gaussianos y sus múltiples aplicaciones.Gracias a mi madre Jacqueline, mi hermano Fran, mis abuelos Mª Carmen y Paco, mi padre Luis y Eva que siempre me han brindado su apoyo incondicional. Como bien sabéis sois fundamentales para mí, por ello ésta tesis también va dedicada a vosotros. No me puedo olvidar de Rosa que ocupa una parte grande de mi corazón. Os quiero.A Duke y Tom por todas esas horas de compañía desinteresada que me han ofrecido durante sus vidas y en especial en la época de redacción de este manuscrito.Por último, quiero dedicar especialmente este trabajo al motor de mi vida, a la persona que tiene que lidiar conmigo en los momentos más difíciles, a la persona que siempre me acaba sacando una sonrisa, a la persona que me enseña día tras día lo que es amor, fuerza, lucha, perseverancia e infinidad de cualidades que la definen. Gracias por todo cariño, sin tu apoyo estoy seguro de que esto no hubiera sido posible. Durante estos 10 años hemos abierto y cerrado muchas etapas juntos pero lo más ilusionante son todas aquellas que nos quedan por vivir. vi AbstractIn last years, the number of blindness cases has been significan...

show abstract

Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion

Cited by 138 publications

References 27 publications

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Segmentation of Exudates via Color-based K-means Clustering and Statistical-based Thresholding

Fundus image analysis for automatic screening of ophthalmic pathologies.

Contact Info

Product

Resources

About