The ellipselet transform

Khodabandeh, Zahra; Rabbani, Hossein; Dehnavi, Alireza Mehri; Sarrafzadeh, Omid

doi:10.4103/jmss.jmss_42_17

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…Therefore, in this study several geometrical X-let transforms including 2D-DWT (11, 25, 26) (Note that Haar wavelet was taken in current study), DTCWT (14, 27) (Note that just the real parts of this transform are utilized in this research to reduce the complexity and redundancy), shearlets (28, 29), contourlets (30), circlets (31), and ellipselets (24) were applied to decompose each B-scan a linear combination of basis functions or dictionary atoms. The non-subsampled (NS) (32) form of the multi-scale X-let transforms was employed to build a multi-channel matrix for each B-scan using all the sub-bands in parallel.…”

Section: Methodsmentioning

confidence: 99%

“…The main purpose of this study is to compare the effect of different geometrical X-let transforms, in two or higher dimensions, for OCT classification. These transforms are provided by directional time-frequency dictionaries (24), and they give powerful insight into an image's spatial and frequency characteristics. X-lets are available mathematical tools that provide an intuitive framework for the representation and storage of multi-scale images (21).…”

Section: X-let Transformsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: X-let Transformsmentioning

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

Self Cite

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“…Deep learning gained significant attention in recent years as it demonstrated remarkable performance in image segmentation [ 16 ]. Moreover, directional X-lets are a family of transforms that capture directional information in images [ 17 ]. In this section, we provide an overview of these methods and their applications in the field of ophthalmology, particularly for the cyst segmentation of retinal OCT images.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms

et al. 2023

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The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). Optical Coherence Tomography (OCT) is a vital retinal imaging technology. X-lets (such as curvelet, DTCWT, contourlet, etc.) have several benefits in image processing and analysis. They can capture both local and non-local features of an image simultaneously. The aim of this paper is to propose an optimal deep learning architecture based on sparse basis functions for the automated segmentation of cystic areas in OCT images. Different X-let transforms were used to produce different network inputs, including curvelet, Dual-Tree Complex Wavelet Transform (DTCWT), circlet, and contourlet. Additionally, three different combinations of these transforms are suggested to achieve more accurate segmentation results. Various metrics, including Dice coefficient, sensitivity, false positive ratio, Jaccard index, and qualitative results, were evaluated to find the optimal networks and combinations of the X-let’s sub-bands. The proposed network was tested on both original and noisy datasets. The results show the following facts: (1) contourlet achieves the optimal results between different combinations; (2) the five-channel decomposition using high-pass sub-bands of contourlet transform achieves the best performance; and (3) the five-channel decomposition using high-pass sub-bands formations out-performs the state-of-the-art methods, especially in the noisy dataset. The proposed method has the potential to improve the accuracy and speed of the segmentation process in clinical settings, facilitating the diagnosis and treatment of retinal diseases.

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“…Regarding classification, non-data-adaptive transforms have the advantage that the information in the transform domain remains comparable. Non-data-adaptive transforms refer to transformations that are obtained without considering the specific nature and structure of the data, and they are computed using a predetermined equation 15 . Among non-data-adaptive models, X-let transforms based on multi-scale time/space-frequency analysis are particularly powerful, as they establish connections between frequency and time information.…”

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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The ellipselet transform

Cited by 10 publications

References 18 publications

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

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

Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms

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

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