A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone

Alipour, Shirin Hajeb Mohammad; Rabbani, Hossein; Akhlaghi, Mohammadreza

doi:10.1007/s11760-013-0530-6

Cited by 32 publications

(10 citation statements)

References 38 publications

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“…In this section, the validity of the proposed approach has been investigated. For testing the algorithm, fundus fluorescein angiogram photographs of diabetic patients ( https://misp.mui.ac.ir/en/fundus-fluorescein-angiogram-photographs-diabetic-patients-0 )[ 39 ] has been used. The dataset contains 70 images of size 576 × 720 with 30 normal and 40 abnormal cases.…”

Section: Simulation Resultsmentioning

confidence: 99%

Tensor Ring Based Image Enhancement

Sedighin

2024

Journal of Medical Signals &Amp; Sensors

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Background: Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement. Method: In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm. Result: This enables the method to do the super-resolution and de-noising simultaneously. Conclusion: Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.

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Section: Simulation Resultsmentioning

confidence: 99%

Tensor Ring Based Image Enhancement

Sedighin

2024

Journal of Medical Signals &Amp; Sensors

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“…Other authors, such as Díaz et al [ 30 ], also studied the automated segmentation of the FAZ in healthy and diabetic patients and their comparison with manual segmentation, obtaining poor results in the FAZ that were irregular and did not follow the acircularity, especially in the 3 × 3

OCTA protocol. Although many studies are available regarding different image processing techniques for automatic segmentation of the FAZ in various retinal imaging modalities [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], studies focusing on automatic FAZ segmentation in OCTA were usually conducted on healthy subjects. In addition, a few studies assessing the accuracy of FAZ delineation in OCTA images of diabetic eyes have obtained poor results due to the high incidence of signal noise and artefacts in OCTA imaging of diabetic patients [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although many studies are available regarding different image processing techniques for automatic segmentation of the FAZ in various retinal imaging modalities [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], studies focusing on automatic FAZ segmentation in OCTA were usually conducted on healthy subjects. In addition, a few studies assessing the accuracy of FAZ delineation in OCTA images of diabetic eyes have obtained poor results due to the high incidence of signal noise and artefacts in OCTA imaging of diabetic patients [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. However, some authors such as Liu et al proposed the application of the watershed algorithm in FAZ segmentation for analysing and diagnosing eye diseases [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

A Reliable Criterion for the Correct Delimitation of the Foveal Avascular Zone in Diabetic Patients

Fernández-Espinosa,

Ruiz-Tabuenca,

Orduna-Hospital

et al. 2023

JPM

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Background: Manual segmentation of the Foveal Avascular Zone (FAZ) has a high level of variability. Research into retinas needs coherent segmentation sets with low variability. Methods: Retinal optical coherence tomography angiography (OCTA) images from type-1 diabetes mellitus (DM1), type-2 diabetes mellitus (DM2) and healthy patients were included. Superficial (SCP) and deep (DCP) capillary plexus FAZs were manually segmented by different observers. After comparing the results, a new criterion was established to reduce variability in the segmentations. The FAZ area and acircularity were also studied. Results: The new segmentation criterion produces smaller areas (closer to the real FAZ) with lower variability than the different criteria of the explorers in both plexuses for the three groups. This was particularly noticeable for the DM2 group with damaged retinas. The acircularity values were also slightly reduced with the final criterion in all groups. The FAZ areas with lower values showed slightly higher acircularity values. We also have a consistent and coherent set of segmentations with which to continue our research. Conclusions: Manual segmentations of FAZ are generally carried out with little attention to the consistency of the measurements. A novel criterion for segmenting the FAZ allows segmentations made by different observers to be more similar.

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“…As the wavelet’s basis is isotropic, and the curve has direction, so the wavelet requires many more coefficients to account for the edges. By applying the Curvelet transform and modifying its coefficients, objects and features can be made more distinguishable ( 20 ). This property makes DCT as efficient tool for analyzing medical images, because they contain several curved-shaped objects.…”

Section: Background and Proposed Algorithmmentioning

confidence: 99%

A novel algorithm for PET and MRI fusion based on digital curvelet transform via extracting lesions on both images

Alipour¹,

Houshyari²,

Mostaar³

2017

Electron Physician

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Background and aimMerging multimodal images is a useful tool for accurate and efficient diagnosis and analysis in medical applications. The acquired data are a high-quality fused image that contains more information than an individual image. In this paper, we focus on the fusion of MRI gray scale images and PET color images.MethodsFor the fusion of MRI gray scale images and PET color images, we used lesion region extracting based on the digital Curvelet transform (DCT) method. As curvelet transform has a better performance in detecting the edges, regions in each image are perfectly segmented. Curvelet decomposes each image into several low- and high-frequency sub-bands. Then, the entropy of each sub-band is calculated. By comparing the entropies and coefficients of the extracted regions, the best coefficients for the fused image are chosen. The fused image is obtained via inverse Curvelet transform. In order to assess the performance, the proposed method was compared with different fusion algorithms, both visually and statistically.ResultThe analysis of the results showed that our proposed algorithm has high spectral and spatial resolution. According to the results of the quantitative fusion metrics, this method achieves an entropy value of 6.23, an MI of 1.88, and an SSIM of 0.6779. Comparison of these experiments with experiments of four other common fusion algorithms showed that our method is effective.ConclusionThe fusion of MRI and PET images is used to gather the useful information of both source images into one image, which is called the fused image. This study introduces a new fusion algorithm based on the digital Curvelet transform. Experiments show that our method has a high fusion effect.

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A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone

Cited by 32 publications

References 38 publications

Tensor Ring Based Image Enhancement

Tensor Ring Based Image Enhancement

A Reliable Criterion for the Correct Delimitation of the Foveal Avascular Zone in Diabetic Patients

A novel algorithm for PET and MRI fusion based on digital curvelet transform via extracting lesions on both images

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