Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Mousavi, Elahe; Kafieh, Rahele; Rabbani, Hossein

doi:10.1049/iet-ipr.2018.6186

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Concatenation of these counts are called HOG descriptor [12]. As HOG feature descriptor can well describe the distribution of local intensity gradients and edge directions, researchers extract HOG feature descriptor from OCT images to train classifier in order to classify diseases automatically [13], [14]. In this paper, we extracted HOG features by using MATLAB command ''extractHOGFeatures''.…”

Section: ) Histograms Of Oriented Gradient Descriptorsmentioning

confidence: 99%

Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

Wang

Zhang

et al. 2020

IEEE Access

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In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.

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Section: ) Histograms Of Oriented Gradient Descriptorsmentioning

confidence: 99%

Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

Wang

Zhang

et al. 2020

IEEE Access

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“…In this abnormality excess blood glucose damages blood vessels in the retina, causing them to leak. This leakage causes an accumulation of fluid in the macula that makes it swell (4, 6). Figure 1 shows an example of normal, DME, and AMD B-scans.…”

Section: Introductionmentioning

confidence: 99%

CircWaveNet: A New Conventional Neural Network Based on Combination of Circlets and Wavelets for Macular OCT Classification

Arian,

Vard,

Kafieh

et al. 2023

Preprint

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Computer-aided systems can help the ophthalmologists in early detection of most of ocular abnormalities using retinal OCT images. The need for more accurate diagnosis increases the need for modifications and innovations to current algorithms. In this paper, we investigate the effect of different X-lets on the classification of OCT B-scans of a dataset with one normal class and two abnormal classes. Different transforms of each B-scan have been fed to the designed 2D-Convolutional-Neural-Network (2D-CNN) to extract the best-suited features. We compare the performance of them with MSVM and MLP classifiers. Comparison with the accuracy of normal and abnormal classes reveals substantially better results for normal cases using 2D-Discrete-Wavelet-Transform (2D-DWT), since the structure of most normal B-scans follows a pattern with zero-degree lines, while for abnormalities with circles appearing in the retinal structure (due to the accumulation of fluid), the circlet transform performs much better. Therefore, we combine these two X-lets and propose a new transform named CircWave which uses all sub-bands of both transformations in the form of a multi-channel-matrix, with the aim to increase the classification accuracy of normal and abnormal cases, simultaneously. We show that the classification results obtained based on CircWave transform outperform those based on the original images and each individual transform. Furthermore, the Grad-CAM class activation visualization for B-scans reconstructed from half of the CircWave sub-bands indicates a greater focus on appearing circles in abnormal cases and straight lines in normal cases at the same time, while for original B-scans the focus of the heat-map is on some irrelevant regions. To investigate the generalizability of our proposed method we have applied it also to another dataset. Using the CircWave transform, we have obtained an accuracy of 94.5% and 90% for the first and second dataset, respectively, while these values were 88% and 83% using the original images. The proposed CNN based on CircWave provides not only superior evaluation parameter values but also better interpretable results with more focus on features that are important for ophthalmologists.

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“…In this abnormality, excess blood glucose damages blood vessels in the retina, causing them to leak. This leakage results in an accumulation of fluid in the macula, causing it to swell 4 , 6 . Figure 1 shows examples of normal, DME, and AMD eyes along with their corresponding B-scans.…”

Section: Introductionmentioning

confidence: 99%

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Arian,

Vard,

Kafieh

et al. 2023

Sci Rep

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Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time–frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time–frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.

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Classification of dry age‐related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

Cited by 11 publications

References 20 publications

Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

CircWaveNet: A New Conventional Neural Network Based on Combination of Circlets and Wavelets for Macular OCT Classification

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

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