Longitudinal evaluation of morphological, functional and vascular alterations in a rat model of experimental glaucoma.

Glaucoma, the leading cause of irreversible blindness worldwide, is a neurodegenerative disease characterized by chronic axonal damages and progressive loss of retinal ganglion cells, with increased intraocular pressure (IOP) as the primary risk factor. While current treatments focus solely on reducing IOP, understanding glaucoma through experimental models is essential for developing new therapeutic strategies and biomarkers for early diagnosis. Our research group developed an ocular hypertension rat model based on limbal plexus cautery, which provides significant glaucomatous neurodegeneration up to four weeks after injury. We evaluated long-term morphological, functional, and vascular alterations in this model. Our results showed that transient ocular hypertension, lasting approximately one week, can lead to progressive increase in optic nerve cupping and retinal ganglion cells loss. Remarkably, the pressure insult caused several vascular changes, such as arteriolar and venular thinning, and permanent choroidal vascular swelling. This study provides evidence of the longitudinal effects of a pressure insult on retinal structure and function using clinical modalities and techniques. The multifactorial changes reported in this model resemble the complex retinal ganglion cell degeneration found in glaucoma patients, and therefore may also provide a unique tool for the development of novel interventions to either halt or slow down disease progression.

An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.

OBJECTIVE: To evaluate the performance of two lightweight neural network models in the diagnosis of common fundus diseases and make comparison to another two classical models. METHODS: A total of 16,000 color fundus photography were collected, including 2000 each of glaucoma, diabetic retinopathy (DR), high myopia, central retinal vein occlusion (CRVO), age-related macular degeneration (AMD), optic neuropathy, and central serous chorioretinopathy (CSC), in addition to 2000 normal fundus. Fundus photography was obtained from patients or physical examiners who visited the Ophthalmology Department of Beijing Tongren Hospital, Capital Medical University. Each fundus photography has been diagnosed and labeled by two professional ophthalmologists. Two classical classification models (ResNet152 and DenseNet121), and two lightweight classification models (MobileNetV3 and ShufflenetV2), were trained. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the performance of the four models. RESULTS: Compared with the classical classification model, the total size and number of parameters of the two lightweight classification models were significantly reduced, and the classification speed was sharply improved. Compared with the DenseNet121 model, the ShufflenetV2 model took 50.7% less time to make a diagnosis on a fundus photography. The classical models performed better than lightweight classification models, and Densenet121 showed highest AUC in five out of the seven common fundus diseases. However, the performance of lightweight classification models is satisfying. The AUCs using MobileNetV3 model to diagnose AMD, diabetic retinopathy, glaucoma, CRVO, high myopia, optic atrophy, and CSC were 0.805, 0.892, 0.866, 0.812, 0.887, 0.868, and 0.803, respectively. For ShufflenetV2model, the AUCs for the above seven diseases were 0.856, 0.893, 0.855, 0.884, 0.891, 0.867, and 0.844, respectively. CONCLUSION: The training of light-weight neural network models based on color fundus photography for the diagnosis of common fundus diseases is not only fast but also has a significant reduction in storage size and parameter number compared with the classical classification model, and can achieve satisfactory accuracy.

Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity.

Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity from color fundus images. Our methodology takes into consideration several anatomical factors to weigh the importance of each individual blood vessel. First, we use deep neural networks to produce a robust extraction of the different anatomical structures. Then, the weighting coefficients that are required for the integration of the different anatomical factors are adjusted using evolutionary computation. Finally, the proposed methodology also provides visual representations that explain the contribution of each individual blood vessel to the predicted tortuosity, hence allowing us to understand the decisions of the model. We validate our proposal in a dataset of color fundus images providing a consensus ground truth as well as the annotations of five clinical experts. Our proposal outperforms previous automated methods and offers a performance that is comparable to that of the clinical experts. Therefore, our methodology demonstrates to be a viable alternative for the assessment of the retinal vascular tortuosity. This could facilitate the use of this biomarker in clinical practice and medical research.

Retinal Vessel Local Tortuosity under a Macula-to-Optic Disc Central-Framing Change.

Some ocular and cardiovascular diseases can be detected through the increased tortuosity of retinal blood vessels. Objective tortuosity measures can be obtained from digital image analysis of a retinography. This study tested a set of local tortuosity indices under a change in the frame center (macula, optic disc) of the eye fundus image. We illustrate the effects of such a change on 40 pairs of vessels evaluated with eight tortuosity indices. We show that the frame center change caused significant differences in the mean values of the vast majority of the tortuosity indices analyzed. The index defined as the ratio of the curvature to the arc length of a vessel segment proved to be the most robust in relation to a frame center change. Experimental results obtained from the analysis of clinical images are provided and discussed.

Context encoder transfer learning approaches for retinal image analysis.

During the last years, deep learning techniques have emerged as powerful alternatives to solve biomedical image analysis problems. However, the training of deep neural networks usually needs great amounts of labeled data to be done effectively. This is even more critical in the case of biomedical imaging due to the added difficulty of obtaining data labeled by experienced clinicians. To mitigate the impact of data scarcity, one of the most commonly used strategies is transfer learning. Nevertheless, the success of this approach depends on the effectiveness of the available pre-training techniques for learning from little or no labeled data. In this work, we explore the application of the Context Encoder paradigm for transfer learning in the domain of retinal image analysis. To this aim, we propose several approaches that allow to work with full resolution images and improve the recognition of the retinal structures. In order to validate the proposals, the Context Encoder pre-trained models are fine-tuned to perform two relevant tasks in the domain: vessels segmentation and fovea localization. The experiments performed on different public datasets demonstrate that the proposed Context Encoder approaches allow mitigating the impact of data scarcity, being superior to previous alternatives in this domain.

Remote ophthalmology with a smartphone adapter handled by nurses for the diagnosis of eye posterior pole pathologies during the COVID-19 pandemic.

INTRODUCTION: The use of smartphones to provide specialist ophthalmology services is becoming a more commonly used method to support patients with eye pathologies. During the COVID-19 pandemic, demand for telehealth services such as tele-ophthalmology, is increasing rapidly. METHODS: In 2019, the agreement between diagnostic tests was investigated by comparing the diagnostic performance for eye posterior pole pathologies of the images obtained by a smartphone coupled to a medical device known as open retinoscope (OR), handled by a nurse and subsequently assessed by an ophthalmologist versus the images obtained by an ophthalmologist using a slit lamp associated to a 76 diopter indirect ophthalmic lens (Volk Super FieldVR ) (SL-IOL) at the outpatient department of a hospital. The OR used in this study worked with a 28 diopter indirect lens. RESULTS: An examination of 151 dilated eyes (79 adult patients, mean age of 66.7 years, 59.5% women) was conducted. Sensitivity was 98.9%, specificity was 89.8%, the positive predictive value was 93.8% and the negative predictive value was 98.2%. The kappa index between both tests was 0.90 (95% CI: 0.83-0.97) in basic diagnosis, 0.81 (95% CI: 0.74-0.89) in syndromic diagnosis (13 categories) and 0.70 (95% CI: 0.62-0.77) in advanced diagnosis (23 categories). DISCUSSION: Images obtained by a nurse using a smartphone coupled to the OR and subsequently assessed by an ophthalmologist showed a high diagnostic performance for eye posterior pole pathologies, which could pave the way for remote ophthalmology systems for this patient group.

Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression.

Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, missing the finer distinction and intrinsic relation between the different possible stages or grades. Ordinal regression (or classification) considers the order of the values of the categorical labels and thus takes into account the order of grading scales used to assess the severity of different medical conditions. This paper presents a quantum-inspired deep probabilistic learning ordinal regression model for medical image diagnosis that takes advantage of the representational power of deep learning and the intrinsic ordinal information of disease stages. The method is evaluated on two different medical image analysis tasks: prostate cancer diagnosis and diabetic retinopathy grade estimation on eye fundus images. The experimental results show that the proposed method not only improves the diagnosis performance on the two tasks but also the interpretability of the results by quantifying the uncertainty of the predictions in comparison to conventional deep classification and regression architectures. The code and datasets are available at https://github.com/stoledoc/DQOR.

Multimodal image encoding pre-training for diabetic retinopathy grading.

Diabetic retinopathy is an increasingly prevalent eye disorder that can lead to severe vision impairment. The severity grading of the disease using retinal images is key to provide an adequate treatment. However, in order to learn the diverse patterns and complex relations that are required for the grading, deep neural networks require very large annotated datasets that are not always available. This has been typically addressed by reusing networks that were pre-trained for natural image classification, hence relying on additional annotated data from a different domain. In contrast, we propose a novel pre-training approach that takes advantage of unlabeled multimodal visual data commonly available in ophthalmology. The use of multimodal visual data for pre-training purposes has been previously explored by training a network in the prediction of one image modality from another. However, that approach does not ensure a broad understanding of the retinal images, given that the network may exclusively focus on the similarities between modalities while ignoring the differences. Thus, we propose a novel self-supervised pre-training that explicitly teaches the networks to learn the common characteristics between modalities as well as the characteristics that are exclusive to the input modality. This provides a complete comprehension of the input domain and facilitates the training of downstream tasks that require a broad understanding of the retinal images, such as the grading of diabetic retinopathy. To validate and analyze the proposed approach, we performed an exhaustive experimentation on different public datasets. The transfer learning performance for the grading of diabetic retinopathy is evaluated under different settings while also comparing against previous state-of-the-art pre-training approaches. Additionally, a comparison against relevant state-of-the-art works for the detection and grading of diabetic retinopathy is also provided. The results show a satisfactory performance of the proposed approach, which outperforms previous pre-training alternatives in the grading of diabetic retinopathy.

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy.

Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.

Topological Data Analysis for Eye Fundus Image Quality Assessment.

The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label "quality" and 0.952 for label "no quality", recall of 0.932 for label "quality" and 0.912 for label "no quality", AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD.

Normalization of series of fundus images to monitor the geographic atrophy growth in dry age-related macular degeneration.

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (ARMD) is a degenerative disease that affects the retina, and the leading cause of visual loss. In its dry form, the pathology is characterized by the progressive, centrifugal expansion of retinal lesions, called geographic atrophy (GA). In infrared eye fundus images, the GA appears as localized bright areas and its growth can be observed in series of images acquired at regular time intervals. However, illumination distortions between the images make impossible the direct comparison of intensities in order to study the GA progress. Here, we propose a new method to compensate for illumination distortion between images. METHODS: We process all images of the series so that any two images have comparable gray levels. Our approach relies on an illumination/reflectance model. We first estimate the pixel-wise illumination ratio between any two images of the series, in a recursive way; then we correct each image against all the others, based on those estimates. The algorithm is applied on a sliding temporal window to cope with large changes in reflectance. We also propose morphological processing to suppress illumination artefacts. RESULTS: The corrected illumination function is homogeneous in the series, enabling the direct comparison of grey-levels intensities in each pixel, and so the detection of the GA growth between any two images. To demonstrate that, we present numerous experiments performed on a dataset of 18 series (328 images), manually segmented by an ophthalmologist. First, we show that the normalization preprocessing dramatically increases the contrast of the GA growth areas. Secondly, we apply segmentation algorithms derived from Otsu's thresholding to detect automatically the GA total growth and the GA progress between consecutive images. We demonstrate qualitatively and quantitatively that these algorithms, although fully automatic, unsupervised and basic, already lead to interesting segmentation results when applied to the normalized images. Colored maps representing the GA evolution can be derived from the segmentations. CONCLUSION: To our knowledge, the proposed method is the first one which corrects automatically and jointly the illumination inhomogeneity in a series of fundus images, regardless of the number of images, the size, shape and progression of lesion areas. This algorithm greatly facilitates the visual interpretation by the medical expert. It opens up the possibility of treating automatically each series as a whole (not just in pairs of images) to model the GA growth.

O ensino do exame de fundo de olho: vivências e percepções de estudantes de medicina / The teaching of ocular fundus exam: experiences and perceptions of medicine students

Resumo: Introdução: O ensino médico vem passando por transformações nas últimas décadas. Objetivos educacionais tendem a se alterar com os avanços tecnológicos recentes, em especial na área de tecnologias de informação. Objetivo: Esta pesquisa aborda o exame do fundo de olho explorando e analisando as dificuldades dos estudantes de Medicina na execução desse componente do exame clínico e busca propor diretrizes para seu ensino na graduação médica. Métodos: Trata-se de uma pesquisa qualitativa com técnicas de observação direta e entrevistas com análise de conteúdo em uma população de estudantes do internato da Universidade do Estado do Pará (Uepa), na cidade de Marabá. Na avaliação de conteúdo utilizaram-se recursos do programa livre de análise de texto Iramuteq. Resultados: Dos 21 estudantes voluntários participantes da pesquisa, apenas dois relataram experiência anterior com oftalmoscópio direto (9,52%) e um aluno havia participado de campanha com uso de dispositivo portátil para registro da imagem do fundo de olho (4,8%). As atividades da pesquisa incluiram discussão de casos clínicos, realização de oftalmoscopias diretas em pacientes voluntários e análise de retinografias. Na análise dos textos correspondentes às entrevistas foram categorizadas quatro classes geradas pelo programa Iramuteq, realçando-se o valor da integração de teoria e prática no depoimento dos alunos. Conclusão: Programas de treinamento com integração de teoria e prática e valendo-se de princípios de aprendizagem significativa podem contribuir para prover competência ao estudante de Medicina para o exame de fundo de olho, adequando-se ao surgimento de novas tecnologias.

Abstract: Introduction: Medical education has undergone changes in recent decades. Educational objectives tend to change with recent technological advances, especially in the area of information technologies. Objective: This research addresses the examination of ocular fundus by exploring and analyzing the difficulties medical students encounter in relation to this component of the clinical examination and seeks to propose guidelines for its teaching in undergraduate medical training. Method: Qualitative research with direct observation techniques and interviews with content analysis with a population of students at the University of the State of Pará (UEPA), in the city of Marabá. Content analysis used resources from the free text analysis program Iramuteq. Results: Of the 21 volunteer students participating in the research, only two reported previous experience with direct ophthalmoscope (9.52%) and one student had participated in a campaign using a portable device to record the fundus image (4.8%). Research activities included discussion of clinical cases, performing direct ophthalmoscopies in volunteer patients and retinography analysis. In the analysis of the texts corresponding to the interviews, four classes generated by the Iramuteq program were categorized, highlighting the value of theoretical and practical integration in the students' testimony. Conclusion: Training programs with theoretical and practical integration using meaningful learning concepts can help qualify medical students in the ocular fundus exam, adapting to the emergence of new technologies.

Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images.

Glaucoma is an optic neuropathy that gradually steals the patient's sight by damaging the optic nerve head (which is responsible for transferring images from the eye to the brain). Causing an estimated 12.3% of global blindness, glaucoma is considered as the first leading cause of irreversible blindness in the world. This paper presents a novel eye fundus image analysis algorithm for the automatic measurement of fundus related glaucoma indicators; Cup to Disc Ratio (CDR), verification of the ISNT rule, Disc Damage Likelihood Scale (DDLS), and the classification of the input fundus into glaucoma or non-glaucoma case using a random forest model. The proposed method is applied on the public image database 'HRF', and a local database containing both, normal and glaucoma cases, and resulted sensitivity, specificity, and accuracy of 1, 0.93 and 0.97 respectively. This technique presented the highest classification accuracy compared to previous works studied in the state of the art; hence, it can be used as a computer aided glaucoma diagnosis system by ophthalmologists to assist in their screening routine.

Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images.

BACKGROUND AND OBJECTIVES: The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images. METHOD: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks. RESULTS: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin. CONCLUSIONS: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions.

Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.

Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automated image assessment system, which can benefit from the development of deep learning techniques. Therefore, the effective classification performance is significant in favor of the referable DR identification task. In this paper, we propose a new strategy, which applies multiple weighted paths into convolutional neural network, called the WP-CNN, motivated by the ensemble learning. In WP-CNN, multiple path weight coefficients are optimized by back propagation, and the output features are averaged for redundancy reduction and fast convergence. The experiment results show that with the efficient training convergence rate WP-CNN achieves an accuracy of 94.23% with sensitivity of 90.94%, specificity of 95.74%, an area under the receiver operating curve of 0.9823 and F1-score of 0.9087. By taking full advantage of the multipath mechanism, the proposed WP-CNN is shown to be accurate and effective for referable DR identification compared to the state-of-art algorithms.

Automated System for Referral of Cotton-Wool Spots.

BACKGROUND: Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in the diagnosis of the disease and provides better medical attention. METHODS: In this paper, an automated system for the detection of soft exudates has been suggested. The system has been developed by the combination of different techniques like Scale Invariant Feature Transform (SIFT), Visual Dictionaries, K-means clustering and Support Vector Machine (SVM). RESULTS: The performance of the system is evaluated on a publically available dataset and AUC of 94.59% is achieved with the highest accuracy obtained is 94.59%. The experiments are also performed after mixing three datasets and AUC of 92.61% is observed with 91.94% accuracy. CONCLUSION: The proposed system is easy to implement and can be used by medical experts both online and offline for referral of Cotton-wool spots in large populations. The system shows promising performance.

Our experience with smartphone and spherical lens for the eye fundus examination during humanitarian project in Africa.

To present the experience of eye fundus photo documentation by using the plus 20 diopters spherical Volk lens and a smartphone with 4.2 Mpix camera and LED flash within the screening project of eye disorders in countries where the standard ophthalmology equipment is not available. Totally 241 patients underwent ophthalmology screening examination. The documentation of the eye fundus included patients with Burkitt lymphoma, Kala Azar, malnutrition with unknown etiology, tuberculosis, HIV positive patients, Usher syndrome and hypertension. This technique as an alternative way of screening will become a standard within examination of patients with eye disorders in outfield regions of developing countries.

Automatic detection of microaneurysms in retinal fundus images.

Diabetic retinopathy (DR) is one of the leading causes of new cases of blindness. Early and accurate detection of microaneurysms (MAs) is important for diagnosis and grading of diabetic retinopathy. In this paper, a new method for the automatic detection of MAs in eye fundus images is proposed. The proposed method consists of four main steps: preprocessing, candidate extraction, feature extraction and classification. A total of 27 characteristic features which contain local features and profile features are extracted for KNN classifier to distinguish true MAs from spurious candidates. The proposed method has been evaluated on two public database: ROC and e-optha. The experimental result demonstrates the efficiency and effectiveness of the proposed method, and it has the potential to be used to diagnose DR clinically.

Semi-automatic measuring of arteriovenous relation as a possible silent brain infarction risk index in hypertensive patients. / Medición semiautomática de la relación arteriovenosa retiniana como posible marcador de riesgo de infarto cerebral silente en pacientes hipertensos.

OBJECTIVE: To evaluate the usefulness of a semiautomatic measuring system of arteriovenous relation (RAV) from retinographic images of hypertensive patients in assessing their cardiovascular risk and silent brain ischemia (ICS) detection. METHODS: Semi-automatic measurement of arterial and venous width were performed with the aid of Imedos software and conventional fundus examination from the analysis of retinal images belonging to the 976 patients integrated in the cohort Investigating Silent Strokes in Hypertensives: a magnetic resonance imaging study (ISSYS), group of hypertensive patients. All patients have been subjected to a cranial magnetic resonance imaging (RMN) to assess the presence or absence of brain silent infarct. RESULTS: Retinal images of 768 patients were studied. Among the clinical findings observed, association with ICS was only detected in patients with microaneurysms (OR 2.50; 95% CI: 1.05-5.98) or altered RAV (<0.666) (OR: 4.22; 95% CI: 2.56-6.96). In multivariate logistic regression analysis adjusted by age and sex, only altered RAV continued demonstrating as a risk factor (OR: 3.70; 95% CI: 2.21-6.18). CONCLUSIONS: The results show that the semiautomatic analysis of the retinal vasculature from retinal images has the potential to be considered as an important vascular risk factor in hypertensive population.

A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images.

Diabetes increases the risk of developing any deterioration in the blood vessels that supply the retina, an ailment known as Diabetic Retinopathy (DR). Since this disease is asymptomatic, it can only be diagnosed by an ophthalmologist. However, the growth of the number of ophthalmologists is lower than the growth of the population with diabetes so that preventive and early diagnosis is difficult due to the lack of opportunity in terms of time and cost. Preliminary, affordable and accessible ophthalmological diagnosis will give the opportunity to perform routine preventive examinations, indicating the need to consult an ophthalmologist during a stage of non proliferation. During this stage, there is a lesion on the retina known as microaneurysm (MA), which is one of the first clinically observable lesions that indicate the disease. In recent years, different image processing algorithms, which allow the detection of the DR, have been developed; however, the issue is still open since acceptable levels of sensitivity and specificity have not yet been reached, preventing its use as a pre-diagnostic tool. Consequently, this work proposes a new approach for MA detection based on (1) reduction of non-uniform illumination; (2) normalization of image grayscale content to improve dependence of images from different contexts; (3) application of the bottom-hat transform to leave reddish regions intact while suppressing bright objects; (4) binarization of the image of interest with the result that objects corresponding to MAs, blood vessels, and other reddish objects (Regions of Interest-ROIs) are completely separated from the background; (5) application of the hit-or-miss Transformation on the binary image to remove blood vessels from the ROIs; (6) two features are extracted from a candidate to distinguish real MAs from FPs, where one feature discriminates round shaped candidates (MAs) from elongated shaped ones (vessels) through application of Principal Component Analysis (PCA); (7) the second feature is a count of the number of times that the radon transform of the candidate ROI, evaluated at the set of discrete angle values {0°, 1°, 2°, …, 180°}, is characterized by a valley between two peaks. The proposed approach is tested on the public databases DiaretDB1 and Retinopathy Online Challenge (ROC) competition. The proposed MA detection method achieves sensitivity, specificity and precision of 92.32%, 93.87% and 95.93% for the diaretDB1 database and 88.06%, 97.47% and 92.19% for the ROC database. Theory, results, challenges and performance related to the proposed MA detecting method are presented.