Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection

BACKGROUND

The timely diagnosis of medical conditions, particularly diabetic retinopathy, relies on the identification of retinal microaneurysms. However, the commonly used retinography method poses a challenge due to the diminutive dimensions and limited differentiation of microaneurysms in images.

PROBLEM STATEMENT

Automated identification of microaneurysms becomes crucial, necessitating the use of comprehensive ad-hoc processing techniques. Although fluorescein angiography enhances detectability, its invasiveness limits its suitability for routine preventative screening.

OBJECTIVE

This study proposes a novel approach for detecting retinal microaneurysms using a fundus scan, leveraging circular reference-based shape features (CR-SF) and radial gradient-based texture features (RG-TF).

METHODOLOGY

The proposed technique involves extracting CR-SF and RG-TF for each candidate microaneurysm, employing a robust back-propagation machine learning method for training. During testing, extracted features from test images are compared with training features to categorize microaneurysm presence.

RESULTS

The experimental assessment utilized four datasets (MESSIDOR, Diaretdb1, e-ophtha-MA, and ROC), employing various measures. The proposed approach demonstrated high accuracy (98.01%), sensitivity (98.74%), specificity (97.12%), and area under the curve (91.72%).

CONCLUSION

The presented approach showcases a successful method for detecting retinal microaneurysms using a fundus scan, providing promising accuracy and sensitivity. This non-invasive technique holds potential for effective screening in diabetic retinopathy and other related medical conditions.

Detecting red-lesions from retinal fundus images using unique morphological features

One of the most important retinal diseases is Diabetic Retinopathy (DR) which can lead to serious damage to vision if remains untreated. Red-lesions are from important demonstrations of DR helping its identification in early stages. The detection and verification of them is helpful in the evaluation of disease severity and progression. In this paper, a novel image processing method is proposed for extracting red-lesions from fundus images. The method works based on finding and extracting the unique morphological features of red-lesions. After quality improvement of images, a pixel-based verification is performed in the proposed method to find the ones which provide a significant intensity change in a curve-like neighborhood. In order to do so, a curve is considered around each pixel and the intensity changes around the curve boundary are considered. The pixels for which it is possible to find such curves in at least two directions are considered as parts of red-lesions. The simplicity of computations, the high accuracy of results, and no need to post-processing operations are the important characteristics of the proposed method endorsing its good performance.

Multi-Model Domain Adaptation for Diabetic Retinopathy Classification

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

Optimal Feature Selection for Diagnosing Diabetic Retinopathy Using FireFly Migration Operator-Based Monarch Butterfly Optimization

In recent years, diabetic retinopathy (DR) needs to be focused with the intention of developing accurate and effective approaches by accomplishing the existing challenges in the traditional models. With this objective, this paper aims to introduce an effective diagnosis system by utilizing retinal fundus images. The implementation of this diagnosis model incorporates 4 stages like (i) preprocessing, (ii) blood vessel segmentation, (iii) feature extraction, as well as (iv) classification. Originally, the median filter as well as contrast limited adaptive histogram equalization (CLAHE) help to preprocess the image. Moreover, the Fuzzy C Mean (FCM) thresholding is applied for blood vessel segmentation, which generates stochastic clustering of pixels to obtain enhanced threshold values. Further, feature extraction is accomplished by utilizing gray-level run-length matrix (GLRM), local, and morphological transformation-based features. Furthermore, a deep learning (DL) model known as convolutional neural network (CNN) is employed for the diagnosis or classification purpose. As a main novelty, this paper introduces an optimal feature selection as well as classification model. Further, the feature selection is done optimally by FireFly Migration Operator-based Monarch Butterfly Optimization (FM-MBO) which hybridized of the monarch butterfly optimization (MBO) and fire fly (FF) algorithms as the entire adopted extracted features attain higher feature length. Moreover, the proposed FM-MBO algorithm helps for optimizing the count of CNN's convolutional neurons to further improve the performance accuracy. At the end, the enhanced outcomes of the adopted diagnostic scheme are validated via a valuable comparative examination in terms of significant performance measures.

Detection of Diabetic Retinopathy Using Discrete Wavelet Transform with Discrete Meyer in Retinal Images

One of the major complicated issues for extensive term diabetic aspirant is diabetic retinopathy (DR) which is an eye retinal syndrome, leads to blindness. The presence of exudates detects the disease, which can be prevented in the early stages by regular screening. Exudates can be automatically detected through inspecting digital retinal image. To detect the exudates for diagnosis the author proposed an algorithm called K-means Kernel support vector machine Radial basis function (KKR) approach, by the following main stages: extracting vessel and removal of optic disc followed by pre-processing, exudates detection and post processing. Wavelet dependent edge enhancement is used for dark portion separation of exudates in the retinal image by optically designed Wideband bandpass filter. Wavelet toolbox of MATLAB 2018a is used in this KKR algorithm. Statistical and structural texture features can be obtained using K-means segmentation process by integrating Local Binary Pattern (LBP) with Region Of Interest (ROI). Some features are selected and used Neural Network along with Radial Basis Function (RBF) to classify further. The KKR algorithm uses 80 fundus images from DIARETDB1 database and parameters are analyzed such as specificity, sensitivity and accuracy. The results obtained from proposed KKR algorithm have specificity of 81.57%, sensitivity of 87.56% and accuracy of 97.94% respectively.

Red-lesion extraction in retinal fundus images by directional intensity changes' analysis

Diabetic retinopathy (DR) is an important retinal disease threatening people with the long diabetic history. Blood leakage in retina leads to the formation of red lesions in retina the analysis of which is helpful in the determination of severity of disease. In this paper, a novel red-lesion extraction method is proposed. The new method firstly determines the boundary pixels of blood vessel and red lesions. Then, it determines the distinguishing features of boundary pixels of red-lesions to discriminate them from other boundary pixels. The main point utilized here is that a red lesion can be observed as significant intensity changes in almost all directions in the fundus image. This can be feasible through considering special neighborhood windows around the extracted boundary pixels. The performance of the proposed method has been evaluated for three different datasets including Diaretdb0, Diaretdb1 and Kaggle datasets. It is shown that the method is capable of providing the values of 0.87 and 0.88 for sensitivity and specificity of Diaretdb1, 0.89 and 0.9 for sensitivity and specificity of Diaretdb0, 0.82 and 0.9 for sensitivity and specificity of Kaggle. Also, the proposed method has a time-efficient performance in the red-lesion extraction process.

A review on computer-aided recent developments for automatic detection of diabetic retinopathy

Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.

Mathematical morphology for microaneurysm detection in fundus images

Aim:

Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss.

Methods:

The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set.

Results:

The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database.

Conclusion:

In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

Retinal Microaneurysms Detection Using Local Convergence Index Features

Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.

Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms

BACKGROUND

The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients.

AIM

The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients.

METHODS

In this work, a novel fully computerized algorithm is used for the recognition of various diseases in macula using both fundus images and optical coherence tomography (OCT) images. Abnormal blood vessels are segmented using thresholding algorithm. The classification is performed by three different classifiers, namely, the support vector machine (SVM), cascade neural network (CNN) and partial least square (PLS) classifiers, which are employed to identify whether the image is normal or abnormal.

CONCLUSION

The results of all of the classifiers are compared based on their accuracy. The classifier accuracies of the SVM, cascade neural network and partial least square are 98.33, 97.16 and 94.34%, respectively. While analysing DME using both images, OCT produced efficient output than fundus images. Information about the severity of the disease and the localization of the pathologies is very useful to the ophthalmologist for diagnosing disease and choosing the proper treatment for a patient to prevent vision loss.

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

Retinal microaneurysms (MAs) are the earliest clinically observable lesions of diabetic retinopathy. Reliable automated MAs detection is thus critical for early diagnosis of diabetic retinopathy. This paper proposes a novel method for the automated MAs detection in color fundus images based on gradient vector analysis and class imbalance classification, which is composed of two stages, i.e. candidate MAs extraction and classification. In the first stage, a candidate MAs extraction algorithm is devised by analyzing the gradient field of the image, in which a multi-scale log condition number map is computed based on the gradient vectors for vessel removal, and then the candidate MAs are localized according to the second order directional derivatives computed in different directions. Due to the complexity of fundus image, besides a small number of true MAs, there are also a large amount of non-MAs in the extracted candidates. Classifying the true MAs and the non-MAs is an extremely class imbalanced classification problem. Therefore, in the second stage, several types of features including geometry, contrast, intensity, edge, texture, region descriptors and other features are extracted from the candidate MAs and a class imbalance classifier, i.e., RUSBoost, is trained for the MAs classification. With the Retinopathy Online Challenge (ROC) criterion, the proposed method achieves an average sensitivity of 0.433 at 1/8, 1/4, 1/2, 1, 2, 4 and 8 false positives per image on the ROC database, which is comparable with the state-of-the-art approaches, and 0.321 on the DiaRetDB1 V2.1 database, which outperforms the state-of-the-art approaches.

Localizing Microaneurysms in Fundus Images Through Singular Spectrum Analysis

GOAL

Reliable recognition of microaneurysms (MAs) is an essential task when developing an automated analysis system for diabetic retinopathy (DR) detection. In this study, we propose an integrated approach for automated MA detection with high accuracy.

METHODS

Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical MA profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true MAs and other non-MA candidates. A set of statistical features of those profiles is then extracted for a K-nearest neighbor classifier.

RESULTS

Experiments show that by applying this process, MAs can be separated well from the retinal background, the most common interfering objects and artifacts.

CONCLUSION

The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity.

SIGNIFICANCE

The approach proposed in the evaluated system has great potential when used in an automated DR screening tool or for large scale eye epidemiology studies.

Automated lesion detectors in retinal fundus images

Diabetic retinopathy (DR) is a sight-threatening condition occurring in persons with diabetes, which causes progressive damage to the retina. The early detection and diagnosis of DR is vital for saving the vision of diabetic persons. The early signs of DR which appear on the surface of the retina are the dark lesions such as microaneurysms (MAs) and hemorrhages (HEMs), and bright lesions (BLs) such as exudates. In this paper, we propose a novel automated system for the detection and diagnosis of these retinal lesions by processing retinal fundus images. We devise appropriate binary classifiers for these three different types of lesions. Some novel contextual/numerical features are derived, for each lesion type, depending on its inherent properties. This is performed by analysing several wavelet bands (resulting from the isotropic undecimated wavelet transform decomposition of the retinal image green channel) and by using an appropriate combination of Hessian multiscale analysis, variational segmentation and cartoon+texture decomposition. The proposed methodology has been validated on several medical datasets, with a total of 45,770 images, using standard performance measures such as sensitivity and specificity. The individual performance, per frame, of the MA detector is 93% sensitivity and 89% specificity, of the HEM detector is 86% sensitivity and 90% specificity, and of the BL detector is 90% sensitivity and 97% specificity. Regarding the collective performance of these binary detectors, as an automated screening system for DR (meaning that a patient is considered to have DR if it is a positive patient for at least one of the detectors) it achieves an average 95-100% of sensitivity and 70% of specificity at a per patient basis. Furthermore, evaluation conducted on publicly available datasets, for comparison with other existing techniques, shows the promising potential of the proposed detectors.

DETECTION AND LOCALIZATION OF RETINAL EXUDATES FOR DIABETIC RETINOPATHY

Diabetic retinopathy (DR) is a complication of diabetes caused by changes in the blood vessels of the retina. Initially, the DR causes trivial changes in the retinal capillary. The symptoms can blur or distort patients' vision, which are the main causes of blindness. The DR is characterized by the presence of exudates at the nonproliferative stage. Once damaged by DR, the effects will be permanent and hence an earlier treatment is considered as vital. The presence of exudates is detected by ophthalmologists from the dilated retinal images, which are captured by dropping chemical solution into the patient's eye that leads to irritation. Therefore, there is a need for an alternative method toward the detection of exudates using image processing algorithms from the nondilated images. In this paper, an automated method is proposed for the detection of exudates using the fuzzy C-Means (FCM) clustering technique and reconstruction through a superimposition process in the absence of dilating patient's eye. The segmented result of FCM is compared with the result obtained using the Fuzzy K-Means segmentation algorithm. The sensitivity and specificity values for the exudates detection using the FCM algorithm are 87.38% and 96.94%, respectively. On the other hand, sensitivity and specificity values for the exudates detection using the K-Means algorithm are 75.04% and 93.73%, respectively.

Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles

An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

A survey on computer aided diagnosis for ocular diseases

Background

Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.

Method

We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed.

Result

We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively.

Conclusion

While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

Retinal microaneurysm detection through local rotating cross-section profile analysis

A method for the automatic detection of microaneurysms (MAs) in color retinal images is proposed in this paper. The recognition of MAs is an essential step in the diagnosis and grading of diabetic retinopathy. The proposed method realizes MA detection through the analysis of directional cross-section profiles centered on the local maximum pixels of the preprocessed image. Peak detection is applied on each profile, and a set of attributes regarding the size, height, and shape of the peak are calculated subsequently. The statistical measures of these attribute values as the orientation of the cross-section changes constitute the feature set that is used in a naïve Bayes classification to exclude spurious candidates. We give a formula for the final score of the remaining candidates, which can be thresholded further for a binary output. The proposed method has been tested in the Retinopathy Online Challenge, where it proved to be competitive with the state-of-the-art approaches. We also present the experimental results for a private image set using the same classifier setup.