A supervised approach for automated detection of hemorrhages in retinal fundus images

Kaur, Navkiran; Chatterjee, Somsirsa; Acharyya, Mausumi; Kaur, Jasmeen; Kapoor, Nishant; Gupta, Sheifali

doi:10.1109/wecon.2016.7993461

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…The authors achieved an SE of 92.6% and an SP of 94% using 219 fundus images. However, hemorrhages near the border of the image aperture were not detected in [84,85], with some bifurcation of vessels present after vascular tree extraction. The detection of HM patterns that are very close to the vascular structure is observed to be a time consuming and difficult task for clinical practitioners.…”

Section: Dr-related Lesion Detection Methodsmentioning

confidence: 92%

“…It describes the respective methods with their utilized dataset and size. Multi-agent model [69] Median filter applied on the green part to estimate the background image, Gaussian and modified kirsch filters applied to more visible dark lesions and to enhance the edges and thickness of pixels [84] implemented a supervised model for hemorrhage detection using 50 retinal images. A morphological closing operation was initially applied for the removal of anatomic components, such as fovea and vessels.…”

Section: Dr-related Lesion Detection Methodsmentioning

confidence: 99%

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Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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Section: Dr-related Lesion Detection Methodsmentioning

confidence: 92%

Section: Dr-related Lesion Detection Methodsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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“…Jayanthi Rajee Bala, Mohamed Mansoor Roomi Sindha, Jency Sahayam, Praveena Govindharaj, and Karthika Priya Rakesh A Morphological operations were carried out to remove Capillaries in order to find HEs then to detect blood leakage Navkiran Kaur et al [9] developed a method for improving the quality of the images, After that, the location of the cornea and optic disc were determined, as well as the capillaries and other features. Finally, the dark spots were found.…”

Section: A Cnn Approach To Central Retinal Vein Occlusion Detectionmentioning

confidence: 99%

A CNN Approach to Central Retinal Vein Occlusion Detection

Bala,

Sindha,

Sahayam

et al. 2023

International Journal of Electronics and Telecommunications

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In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.

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“…These features were given to a random forest classifier as an input to detect hemorrhages, which yielded a sensitivity and specificity of 90.4% and 93.53%, respectively. 31 Because hemorrhages occur in retinal parts adjacent to blood vessels, it is often difficult and time-consuming to detect these lesions.…”

Section: Related Workmentioning

confidence: 99%

Automatic detection of exudates and hemorrhages in low‐contrast color fundus images using multi semantic convolutional neural network

Selçuk

Beyoğlu

Alkan

2022

Concurrency and Computation

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Diabetic retinopathy (DR) is a pathology occurring in the optic nerve due to an excessive blood sugar level in human body. It is one of the major reasons for visual impairment in the developed and developing countries. Patients with DR usually suffer from visual damages due to a high blood sugar level in retinal blood vessel walls. These damages may also leak into other retinal layers of the eye within time. As a result of these leakages and nutritional disorders, a number of lesions such as excudate, edema, microaneurysm, and hemorrhage may occur. In this respect, an accurate and effective detection of these lesions in earlier stages of DR plays an important role in the progression of the disease. In the proposed study, exudate and hemorrhages, which are important clinical findings for DR, were automatically detected from low contrast colored fundus images. Exudate and hemorrhages are lesions with different characteristics. However, in this study, high performance was achieved by making a three-class semantic segmentation. In addition, a color space transformation was performed and the classical U-Net algorithm was provided to achieve stable high performance in low contrast images.Finally, lesion images which were manually detected by a physician were matched with automatically segmented excudate and hemorrhage images using the proposed method. Thus, both segmentation and lesion detection performances of the proposed method were measured. The findings demonstrated that Dice and Jaccard similarity indexes were calculated nearly as 0.95 for the segmentation performance. A sensitivity of 98% and specificity value of 91% were measured for detection performance. It can be inferred from these figures that the proposed method can be effectively used as a supporting system by physicians for the detection and classification of lesions in the color fundus images for the diagnosis of DR.

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A supervised approach for automated detection of hemorrhages in retinal fundus images

Cited by 15 publications

References 5 publications

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

A CNN Approach to Central Retinal Vein Occlusion Detection

Automatic detection of exudates and hemorrhages in low‐contrast color fundus images using multi semantic convolutional neural network

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