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1

Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Pavithra K.C., Kumar Preetham, Geetha M., Bhandary Sulatha V.

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

157--188

EN

Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is characterized by a buildup of extracellular fluid inside the macula through hyperpermeable vessels. The presence of DME can be spotted at any level of DR with varying degrees of severity using prominent imaging modalities such as Color Fundus Photography (CFP) and Optical Coherence Tomography (OCT). Computerized approaches for screening eye disorders appear to be beneficial, as they provide doctors with detailed insights into abnormalities. Such a system for the evaluation of retinal images can function as a stand-alone disease monitoring system. This review reports the state-of-art automated DME detection methods with traditional Machine Learning (ML) and Deep Learning (DL) techniques employing retinal fundus or OCT images. The paper provides a list of public retinal OCT and fundus imaging datasets for DME detection. In addition, the paper describes the dynamics of advancements in presented methods adopted in the past along with their strengths and limitations to highlight the insufficiencies that could be addressed in future investigations.

2

Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection

Toğaçar Mesut, Ergen Burhan, Tümen Vedat

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

646--666

EN

Retinal disease is one of the diseases that cause visual symptoms or loss of vision in humans. This disease can affect the choroid, which severely affects vision. Optical coherence tomography (OCT) images are usually used to detect retinal disease. OCT is an imaging technique that takes high-resolution slices of retinal images. It takes time for experts to examine and interpret the OCT images. Experts need to take advantage of technological capabilities to make this process faster and more accurate. Three datasets were used in this study. Dataset #1 (UCSD dataset) consists of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal OCT image types. Dataset #2 (Duke dataset) and Dataset #3 consist of age-related macular degeneration (AMD), DME, and normal OCT image types. An artificial intelligence based hybrid approach was proposed for retinal disease detection. In the proposed approach, class-based activations were extracted for each model with nine transfer learning models using the dataset. Next, the dominant activations were selected from the model-based activations of each class using the slime mold algorithm (SMA) and the selected activations were classified using the softmax method. The overall accuracy obtained in classification is as follows: 99.60% for dataset 1, 99.89% for dataset #2 and 97.49% for dataset #3. In this study, it was found that the proposed approach contributes to the performance of transfer learning models.

3

Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images

Anoop B. N., Pavan Rakesh, Girish G. N., Kothari Abhishek R., Rajan Jeny

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1343--1358

EN

Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods.

4

Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography

Yang Jianli, Zhang Bo, Wang Hongrui, Lin Feng, Han Yechen, Liu Xiuling

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

719--727

EN

Identification of coronary atherosclerotic plaques and vulnerable plaques is of great clinical significance in the diagnosis and treatment of coronary artery diseases, such as myocardial infarction and sudden death. Optical coherence tomography (OCT) is a catheter-based intracoronary imaging technique with high resolution (<20 μm) adopted to study the morphology of atherosclerotic plaques and identification of the composition of plaques. Nevertheless, manual characterization and quantification of plaques by clinicians is a labor-intensive and subjective procedure. This study aimed to propose a novel plaque characterization algorithm to automatically characterize and classify the atherosclerotic plaques (fibrous, calcific, and lipid-rich). First, nongeometric features such as Fisher vector, principal component analysis, histogram of the oriented gradient, and local binary pattern were investigated and adapted to two geometric features (basic feature and texture feature) to characterize the plaques. Second, for automated classification of the plaques, a hard example mining strategy was introduced to train support vector machine classifier and improve the effectiveness of training data. Third, to demonstrate the relationship between the selected features and the plaque classification accuracy, different feature compositions and comparisons were presented. The contribution of key features to the final classification was revealed. Datasets from 20 OCT pullbacks of 9 patients were used in the training and testing using the proposed algorithm. The overall classification accuracy reached 96.8%, and that of fibrous, calcific, and lipid-rich plaques was 94%, 97.2%, and 99.2%, respectively.

5

Recognition of Weave Patterns of Striped Fabrics Using Optical Coherence Tomography

Sabuncu M., Özdemir H.

Fibres & Textiles in Eastern Europe

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2018

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Vol. 26, no. 3 (129)

98--103

EN

The recognition of woven fabric repeat by conventional techniques is labour intensive. In general, woven fabric repeat identification is accomplished automatically by employing complex algorithms and techniques. These algorithms may, however, occasionally fail, especially when dealing with high complexity texture patterns, structures, figures and colours. Optical coherence tomography (OCT) has the capability of taking high resolution images via contactless measurements. In this paper we apply the spectral domain optical coherence tomography imaging technique for identifying striped woven fabric repeat automatically. OCT scans corresponding to four different fabrics, from which the weave matrixes were recognised, are reported in this study. Automatic identification of weave patterns of striped fabrics was accomplished non-destructively by employing optical coherence tomography.

PL

Rozpoznanie powtórzenia tkaniny za pomocą konwencjonalnych technik jest pracochłonne. Ogólnie rzecz biorąc, identyfikacja powtórzenia tkaniny jest przeprowadzana automatycznie przez zastosowanie złożonych algorytmów i technik. Algorytmy te mogą jednak czasami zawieść, szczególnie w przypadku bardzo złożonych wzorów tekstur, struktur, figur i kolorów. Optyczna tomografia koherencyjna (OCT) ma zdolność wykonywania zdjęć o wysokiej rozdzielczości za pomocą pomiarów bezstykowych. W artykule zastosowano technikę obrazowania optycznego tomografii koherencji spektralnej do automatycznego rozpoznawania powtórzeń tkanin w paski. W badaniu przedstawiono skany OCT odpowiadające czterem różnym materiałom, z których rozpoznano matryce splotu. Automatyczna identyfikacja wzorów splotów tkanin w paski została przeprowadzona w sposób niedestrukcyjny dzięki zastosowaniu optycznej tomografii koherencyjnej.

6

The perspectives of applying OCT technology to the investigations of selected biological materials and processes

Rowińska Z., Gocławski J., Sekulska-Nalewajko J.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2010

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T. 14, z. 3/2

749-764

EN

Optical Coherence Tomography is 3D imaging technology, which can produce high resolution cross-sectional images of biological issues in vivo and in real time. OCT perfectly fills apparent gap in depth/resolution feature space existing between confocal microscopy and ultrasound methods. The paper explains the concept of OCT method and presents the review of some measurement systems currently offered by most advanced producers and some existing applications used in medicine and biology. The authors suggested the new proposals of applying OCT to some biological investigations e.g.: the estimation of topological changes of wheat roots cultivated in the presence of heavy metals, the evaluation of spatial changes of plant tissues caused by biotic and abiotic stressors.

PL

Optyczna tomografia koherencyjna (OCT) jest trójwymiarową technologią obrazowania, która umożliwia uzyskanie wysokiej rozdzielczości obrazów przekrojów obiektów biologicznych in vivo oraz w czasie rzeczywistym. OCT stanowi pośrednie ogniwo w technologiach obrazowania 3D pod względem głębokości wnikania w materiał oraz rozdzielczości, może być zlokalizowana pomiędzy mikroskopią konfokalną a ultrasonografią. Artykuł wyjaśnia koncepcję OCT oraz prezentuje przegląd stanowisk pomiarowych oferowanych przez czołowych producentów oraz niektóre zastosowania w medycynie i biologii. Autorzy wskazali również nowe możliwości wykorzystania tej technologii do pewnych badań w dziedzinie biologii, takich jak: ocena zmian topologii korzeni pszenicy hodowanej w obecności metali ciężkich, ocena przestrzennych zmian strukturalnych tkanek roślin pod wpływem biotycznych i abiotycznych czynników stresowych.