Glaucoma Detection Based on Texture Feature of Neuro Retinal Rim Area in Retinal Fundus Image

One method for detecting glaucoma is by comparing ratios in the area of neuroretinal rim. Comparing area ratios in the neuroretinal rim is difficult for ophthalmologists since it requires high accuracy and is highly dependent on the patient's retinal condition. In this study, we sought to perform neuro retinal rim feature extraction based on histogram and gray level co-occurrence matrix (GLCM) of normal retinal images and glaucoma, automatically distinguish between normal eyes and eyes with glaucoma, and evaluate the method's validity using the measures of accuracy, sensitivity, and specificity We adopted a machine learning approach in conducting automatic feature extraction of the retinal rim through three main stages: 1) image acquisition, 2) pre-processing, and 3) classification. We used a dataset from RIM-ONE for normal eyes images and DRISTHI-GS for glaucoma images.Classification was carried out on 154 images (80 images for glaucoma images and 74 images for normal images). Regarding true positive, false negative, false positive, and true negative, we examined the sensitivity, specificity, and accuracy of automatic extraction and classification. The highest findings are 96.10%, 98.75%, and 93.24%, respectively. This study showed that automatic texture features and classification are possible, accurate and important in detecting glaucoma

Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification

Manuscrito enviado para su revisión por la revista "Engineering Applications of Artificial Intelligence" (Elsevier) el 25 de noviembre de 2022. Se envió la versión revisada el 26 de julio de 2023. El manuscrito fue aceptado el 11 de octubre de 2023, y desde el 28 de octubre aparece el artículo publicado en el portal ScienceDirect (https://doi.org/10.1016/j.engappai.2023.107298).In this work, we evaluate the energy usage of fully embedded medical diagnosis aids based on both segmentation and classification of medical images implemented on Edge TPU and embedded GPU processors. We use glaucoma diagnosis based on color fundus images as an example to show the possibility of performing segmentation and classification in real time on embedded boards and to highlight the different energy requirements of the studied implementations. Several other works develop the use of segmentation and feature extraction techniques to detect glaucoma, among many other pathologies, with deep neural networks. Memory limitations and low processing capabilities of embedded accelerated systems (EAS) limit their use for deep network-based system training. However, including specific acceleration hardware, such as NVIDIA’s Maxwell GPU or Google’s Edge TPU, enables them to perform inferences using complex pre-trained networks in very reasonable times. In this study, we evaluate the timing and energy performance of two EAS equipped with Machine Learning (ML) accelerators executing an example diagnostic tool developed in a previous work. For optic disc (OD) and cup (OC) segmentation, the obtained prediction times per image are under 29 and 43 ms using Edge TPUs and Maxwell GPUs respectively. Prediction times for the classification subsystem are lower than 10 and 14 ms for Edge TPUs and Maxwell GPUs respectively. Regarding energy usage, in approximate terms, for OD segmentation Edge TPUs and Maxwell GPUs use 38 and 190 mJ per image respectively. For fundus classification, Edge TPUs and Maxwell GPUs use 45 and 70 mJ respectively.Manuscrito de 33 páginas

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review.

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?". Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011-2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modelling based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research