Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

[EN] Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869 and GVA through project PROMETEO/2019/109Colomer, A.; Igual García, J.; Naranjo Ornedo, V. (2020). Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Sensors. 20(4):1-20. https://doi.org/10.3390/s20041005S120204World Report on Vision. Technical Report, 2019https://www.who.int/publications-detail/world-report-on-visionFong, D. S., Aiello, L., Gardner, T. W., King, G. L., Blankenship, G., Cavallerano, J. D., … Klein, R. (2003). Retinopathy in Diabetes. Diabetes Care, 27(Supplement 1), S84-S87. doi:10.2337/diacare.27.2007.s84COGAN, D. G. (1961). Retinal Vascular Patterns. Archives of Ophthalmology, 66(3), 366. doi:10.1001/archopht.1961.00960010368014Wilkinson, C. ., Ferris, F. L., Klein, R. E., Lee, P. P., Agardh, C. D., Davis, M., … Verdaguer, J. T. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9), 1677-1682. doi:10.1016/s0161-6420(03)00475-5Universal Eye Health: A Global Action Plan 2014–2019. Technical Reporthttps://www.who.int/blindness/actionplan/en/Salamat, N., Missen, M. M. S., & Rashid, A. (2019). Diabetic retinopathy techniques in retinal images: A review. Artificial Intelligence in Medicine, 97, 168-188. doi:10.1016/j.artmed.2018.10.009Qureshi, I., Ma, J., & Shaheed, K. (2019). A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy. Algorithms, 12(1), 14. doi:10.3390/a12010014Morales, S., Engan, K., Naranjo, V., & Colomer, A. (2017). Retinal Disease Screening Through Local Binary Patterns. IEEE Journal of Biomedical and Health Informatics, 21(1), 184-192. doi:10.1109/jbhi.2015.2490798Asiri, N., Hussain, M., Al Adel, F., & Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine, 99, 101701. doi:10.1016/j.artmed.2019.07.009Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Prentašić, P., & Lončarić, S. (2016). Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Computer Methods and Programs in Biomedicine, 137, 281-292. doi:10.1016/j.cmpb.2016.09.018Costa, P., Galdran, A., Meyer, M. I., Niemeijer, M., Abramoff, M., Mendonca, A. M., & Campilho, A. (2018). End-to-End Adversarial Retinal Image Synthesis. IEEE Transactions on Medical Imaging, 37(3), 781-791. doi:10.1109/tmi.2017.2759102De la Torre, J., Valls, A., & Puig, D. (2020). A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing, 396, 465-476. doi:10.1016/j.neucom.2018.07.102Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., & Frangi, A. F. (2019). Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 38(9), 2211-2218. doi:10.1109/tmi.2019.2903434Walter, T., Klein, J., Massin, P., & Erginay, A. (2002). A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Transactions on Medical Imaging, 21(10), 1236-1243. doi:10.1109/tmi.2002.806290Welfer, D., Scharcanski, J., & Marinho, D. R. (2010). A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Computerized Medical Imaging and Graphics, 34(3), 228-235. doi:10.1016/j.compmedimag.2009.10.001Mookiah, M. R. K., Acharya, U. R., Martis, R. J., Chua, C. K., Lim, C. M., Ng, E. Y. K., & Laude, A. (2013). Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowledge-Based Systems, 39, 9-22. doi:10.1016/j.knosys.2012.09.008Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., Laÿ, B., Danno, R., … Erginay, A. (2014). Exudate detection in color retinal images for mass screening of diabetic retinopathy. Medical Image Analysis, 18(7), 1026-1043. doi:10.1016/j.media.2014.05.004Sopharak, A., Uyyanonvara, B., Barman, S., & Williamson, T. H. (2008). Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized Medical Imaging and Graphics, 32(8), 720-727. doi:10.1016/j.compmedimag.2008.08.009Giancardo, L., Meriaudeau, F., Karnowski, T. P., Li, Y., Garg, S., Tobin, K. W., & Chaum, E. (2012). Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis, 16(1), 216-226. doi:10.1016/j.media.2011.07.004Amel, F., Mohammed, M., & Abdelhafid, B. (2012). Improvement of the Hard Exudates Detection Method Used For Computer- Aided Diagnosis of Diabetic Retinopathy. International Journal of Image, Graphics and Signal Processing, 4(4), 19-27. doi:10.5815/ijigsp.2012.04.03Usman Akram, M., Khalid, S., Tariq, A., Khan, S. A., & Azam, F. (2014). Detection and classification of retinal lesions for grading of diabetic retinopathy. Computers in Biology and Medicine, 45, 161-171. doi:10.1016/j.compbiomed.2013.11.014Akram, M. U., Tariq, A., Khan, S. A., & Javed, M. Y. (2014). Automated detection of exudates and macula for grading of diabetic macular edema. Computer Methods and Programs in Biomedicine, 114(2), 141-152. doi:10.1016/j.cmpb.2014.01.010Quellec, G., Lamard, M., Abràmoff, M. D., Decencière, E., Lay, B., Erginay, A., … Cazuguel, G. (2012). A multiple-instance learning framework for diabetic retinopathy screening. Medical Image Analysis, 16(6), 1228-1240. doi:10.1016/j.media.2012.06.003Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.-C., Meyer, F., … Chabouis, A. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, 34(2), 196-203. doi:10.1016/j.irbm.2013.01.010Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., … Niemeijer, M. (2013). Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy. JAMA Ophthalmology, 131(3), 351. doi:10.1001/jamaophthalmol.2013.1743Almotiri, J., Elleithy, K., & Elleithy, A. (2018). Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Applied Sciences, 8(2), 155. doi:10.3390/app8020155Thakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C. (2000). Image inpainting. Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’00. doi:10.1145/344779.344972Qureshi, M. A., Deriche, M., Beghdadi, A., & Amin, A. (2017). A critical survey of state-of-the-art image inpainting quality assessment metrics. Journal of Visual Communication and Image Representation, 49, 177-191. doi:10.1016/j.jvcir.2017.09.006Colomer, A., Naranjo, V., Engan, K., & Skretting, K. (2017). Assessment of sparse-based inpainting for retinal vessel removal. Signal Processing: Image Communication, 59, 73-82. doi:10.1016/j.image.2017.03.018Morales, S., Naranjo, V., Angulo, J., & Alcaniz, M. (2013). Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology. IEEE Transactions on Medical Imaging, 32(4), 786-796. doi:10.1109/tmi.2013.2238244Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51-59. doi:10.1016/0031-3203(95)00067-4Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. doi:10.1109/tpami.2002.1017623Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Tapia, S. L., Molina, R., & de la Blanca, N. P. (2016). Detection and localization of objects in Passive Millimeter Wave Images. 2016 24th European Signal Processing Conference (EUSIPCO). doi:10.1109/eusipco.2016.7760619Jin Huang, & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 299-310. doi:10.1109/tkde.2005.50Prati, R. C., Batista, G. E. A. P. A., & Monard, M. C. (2011). A Survey on Graphical Methods for Classification Predictive Performance Evaluation. IEEE Transactions on Knowledge and Data Engineering, 23(11), 1601-1618. doi:10.1109/tkde.2011.59Mandrekar, J. N. (2010). Receiver Operating Characteristic Curve in Diagnostic Test Assessment. Journal of Thoracic Oncology, 5(9), 1315-1316. doi:10.1097/jto.0b013e3181ec173dRocha, A., Carvalho, T., Jelinek, H. F., Goldenstein, S., & Wainer, J. (2012). Points of Interest and Visual Dictionaries for Automatic Retinal Lesion Detection. IEEE Transactions on Biomedical Engineering, 59(8), 2244-2253. doi:10.1109/tbme.2012.2201717Júnior, S. B., & Welfer, D. (2013). Automatic Detection of Microaneurysms and Hemorrhages in Color Eye Fundus Images. International Journal of Computer Science and Information Technology, 5(5), 21-37. doi:10.5121/ijcsit.2013.550

Fundus image analysis for automatic screening of ophthalmic pathologies

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745TESI

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

Computational Analysis of Fundus Images: Rule-Based and Scale-Space Models

Fundus images are one of the most important imaging examinations in modern ophthalmology because they are simple, inexpensive and, above all, noninvasive. Nowadays, the acquisition and storage of highresolution fundus images is relatively easy and fast. Therefore, fundus imaging has become a fundamental investigation in retinal lesion detection, ocular health monitoring and screening programmes. Given the large volume and clinical complexity associated with these images, their analysis and interpretation by trained clinicians becomes a timeconsuming task and is prone to human error. Therefore, there is a growing interest in developing automated approaches that are affordable and have high sensitivity and specificity. These automated approaches need to be robust if they are to be used in the general population to diagnose and track retinal diseases. To be effective, the automated systems must be able to recognize normal structures and distinguish them from pathological clinical manifestations. The main objective of the research leading to this thesis was to develop automated systems capable of recognizing and segmenting retinal anatomical structures and retinal pathological clinical manifestations associated with the most common retinal diseases. In particular, these automated algorithms were developed on the premise of robustness and efficiency to deal with the difficulties and complexity inherent in these images. Four objectives were considered in the analysis of fundus images. Segmentation of exudates, localization of the optic disc, detection of the midline of blood vessels, segmentation of the vascular network and detection of microaneurysms. In addition, we also evaluated the detection of diabetic retinopathy on fundus images using the microaneurysm detection method. An overview of the state of the art is presented to compare the performance of the developed approaches with the main methods described in the literature for each of the previously described objectives. To facilitate the comparison of methods, the state of the art has been divided into rulebased methods and machine learningbased methods. In the research reported in this paper, rulebased methods based on image processing methods were preferred over machine learningbased methods. In particular, scalespace methods proved to be effective in achieving the set goals. Two different approaches to exudate segmentation were developed. The first approach is based on scalespace curvature in combination with the local maximum of a scalespace blob detector and dynamic thresholds. The second approach is based on the analysis of the distribution function of the maximum values of the noise map in combination with morphological operators and adaptive thresholds. Both approaches perform a correct segmentation of the exudates and cope well with the uneven illumination and contrast variations in the fundus images. Optic disc localization was achieved using a new technique called cumulative sum fields, which was combined with a vascular enhancement method. The algorithm proved to be reliable and efficient, especially for pathological images. The robustness of the method was tested on 8 datasets. The detection of the midline of the blood vessels was achieved using a modified corner detector in combination with binary philtres and dynamic thresholding. Segmentation of the vascular network was achieved using a new scalespace blood vessels enhancement method. The developed methods have proven effective in detecting the midline of blood vessels and segmenting vascular networks. The microaneurysm detection method relies on a scalespace microaneurysm detection and labelling system. A new approach based on the neighbourhood of the microaneurysms was used for labelling. Microaneurysm detection enabled the assessment of diabetic retinopathy detection. The microaneurysm detection method proved to be competitive with other methods, especially with highresolution images. Diabetic retinopathy detection with the developed microaneurysm detection method showed similar performance to other methods and human experts. The results of this work show that it is possible to develop reliable and robust scalespace methods that can detect various anatomical structures and pathological features of the retina. Furthermore, the results obtained in this work show that although recent research has focused on machine learning methods, scalespace methods can achieve very competitive results and typically have greater independence from image acquisition. The methods developed in this work may also be relevant for the future definition of new descriptors and features that can significantly improve the results of automated methods.As imagens do fundo do olho são hoje um dos principais exames imagiológicos da oftalmologia moderna, pela sua simplicidade, baixo custo e acima de tudo pelo seu carácter nãoinvasivo. A aquisição e armazenamento de imagens do fundo do olho com alta resolução é também relativamente simples e rápida. Desta forma, as imagens do fundo do olho são um exame fundamental na identificação de alterações retinianas, monitorização da saúde ocular, e em programas de rastreio. Considerando o elevado volume e complexidade clínica associada a estas imagens, a análise e interpretação das mesmas por clínicos treinados tornase uma tarefa morosa e propensa a erros humanos. Assim, há um interesse crescente no desenvolvimento de abordagens automatizadas, acessíveis em custo, e com uma alta sensibilidade e especificidade. Estas devem ser robustas para serem aplicadas à população em geral no diagnóstico e seguimento de doenças retinianas. Para serem eficazes, os sistemas de análise têm que conseguir detetar e distinguir estruturas normais de sinais patológicos. O objetivo principal da investigação que levou a esta tese de doutoramento é o desenvolvimento de sistemas automáticos capazes de detetar e segmentar as estruturas anatómicas da retina, e os sinais patológicos retinianos associados às doenças retinianas mais comuns. Em particular, estes algoritmos automatizados foram desenvolvidos segundo as premissas de robustez e eficácia para lidar com as dificuldades e complexidades inerentes a estas imagens. Foram considerados quatro objetivos de análise de imagens do fundo do olho. São estes, a segmentação de exsudados, a localização do disco ótico, a deteção da linha central venosa dos vasos sanguíneos e segmentação da rede vascular, e a deteção de microaneurismas. De acrescentar que usando o método de deteção de microaneurismas, avaliouse também a capacidade de deteção da retinopatia diabética em imagens do fundo do olho. Para comparar o desempenho das metodologias desenvolvidas neste trabalho, foi realizado um levantamento do estado da arte, onde foram considerados os métodos mais relevantes descritos na literatura para cada um dos objetivos descritos anteriormente. Para facilitar a comparação entre métodos, o estado da arte foi dividido em metodologias de processamento de imagem e baseadas em aprendizagem máquina. Optouse no trabalho de investigação desenvolvido pela utilização de metodologias de análise espacial de imagem em detrimento de metodologias baseadas em aprendizagem máquina. Em particular, as metodologias baseadas no espaço de escalas mostraram ser efetivas na obtenção dos objetivos estabelecidos. Para a segmentação de exsudados foram usadas duas abordagens distintas. A primeira abordagem baseiase na curvatura em espaço de escalas em conjunto com a resposta máxima local de um detetor de manchas em espaço de escalas e limiares dinâmicos. A segunda abordagem baseiase na análise do mapa de distribuição de ruído em conjunto com operadores morfológicos e limiares adaptativos. Ambas as abordagens fazem uma segmentação dos exsudados de elevada precisão, além de lidarem eficazmente com a iluminação nãouniforme e a variação de contraste presente nas imagens do fundo do olho. A localização do disco ótico foi conseguida com uma nova técnica designada por campos de soma acumulativos, combinada com métodos de melhoramento da rede vascular. O algoritmo revela ser fiável e eficiente, particularmente em imagens patológicas. A robustez do método foi verificada pela sua avaliação em oito bases de dados. A deteção da linha central dos vasos sanguíneos foi obtida através de um detetor de cantos modificado em conjunto com filtros binários e limiares dinâmicos. A segmentação da rede vascular foi conseguida com um novo método de melhoramento de vasos sanguíneos em espaço de escalas. Os métodos desenvolvidos mostraram ser eficazes na deteção da linha central dos vasos sanguíneos e na segmentação da rede vascular. Finalmente, o método para a deteção de microaneurismas assenta num formalismo de espaço de escalas na deteção e na rotulagem dos microaneurismas. Para a rotulagem foi utilizada uma nova abordagem da vizinhança dos candidatos a microaneurismas. A deteção de microaneurismas permitiu avaliar também a deteção da retinopatia diabética. O método para a deteção de microaneurismas mostrou ser competitivo quando comparado com outros métodos, em particular em imagens de alta resolução. A deteção da retinopatia diabética exibiu um desempenho semelhante a outros métodos e a especialistas humanos. Os trabalhos descritos nesta tese mostram ser possível desenvolver uma abordagem fiável e robusta em espaço de escalas capaz de detetar diferentes estruturas anatómicas e sinais patológicos da retina. Além disso, os resultados obtidos mostram que apesar de a pesquisa mais recente concentrarse em metodologias de aprendizagem máquina, as metodologias de análise espacial apresentam resultados muito competitivos e tipicamente independentes do equipamento de aquisição das imagens. As metodologias desenvolvidas nesta tese podem ser importantes na definição de novos descritores e características, que podem melhorar significativamente o resultado de métodos automatizados

Automated Retinal Lesion Detection via Image Saliency Analysis

Background and objective:The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods :Retinal images are firstly segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disc, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at pixel-level from different modalities of retinal images, without the need to tune parameters. Results:To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at pixel-level, lesion-level, or image-level according to ground truth availability in these datasets. Conclusions:The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy

A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures

Application of deep learning techniques for biomedical data analysis

Deep learning and machine learning methods have been used for addressing the problems in the biomedical applications, such as diabetic retinopathy assessment and Parkinson's disease diagnosis. The severity of diabetic retinopathy is estimated by the expert's examination of fundus images based on the amount and location of three diabetic retinopathy signs (i.e., exudates, hemorrhages, and microaneurysms). An automatic and accurate system for detection of these signs can significantly help clinicians to make the best possible prognosis can result in reducing the risk of vision loss. For Parkinson's disease diagnosis, analysis of a speech voice is considered as the earliest symptom with the advantage of being non-intrusive and suitable for online applications. While some reported outcomes of the developed techniques have shown the good results and ongoing progress for these two applications, designing new algorithms is a thriving research field to overcome the poor sensitivity and specificity of the outcomes as well as the limitations such as dataset size and heuristic selection of the network parameters. This thesis has comprehensively studied and developed various deep learning frameworks for detection of diabetic retinopathy signs and diagnosis of Parkinson's disease. To improve the performance of the current systems, this work has had an investigation on different techniques: (i) color space investigation, (ii) examination of various deep learning methods, (iii) development of suitable pre/post-processing algorithms and (iv) appropriate selection of deep learning architectures and parameters. For diabetic retinopathy assessment, this thesis has proposed the new color space as the input for the deep learning models that obtained better replicability compared with the conventional color spaces. This has also shown the pre-trained model can extract more relevant features compared to the models which were trained from scratch. This has also presented a deep learning framework combined with the suitable pre and post-processing algorithms that increased the performance of the system. By investigation different architectures and parameters, the suitable deep learning model has been presented to distinguish between Parkinson's disease and healthy speech signal