Attenuation correction in SPECT images using attenuation map estimation with its emission data

Tavakoli, Meysam; Naji, Maryam; Abdollahi, Ali; Kalantari, Faraz

doi:10.1117/12.2254828

Cited by 7 publications

(8 citation statements)

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“…58 Moreover, computers are suitable to issues involving the derivation of quantitative information from images because of their capacity to process data in fast and efficient manner with a high degree of reproducibility. [59][60][61] In this paper, we proposed a hierarchical method based on combination of matching based approach and CNNs to detect all MAs from color fundus retinal image. Although the results of MAs detection without vessel masking was promising, to avoid more false positive and screen more patients which they dont need this is better to segment and mask retinal vessel as a preprocessed task before final MAs detection.…”

Section: Resultsmentioning

confidence: 99%

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli

Nazar

Mehdizadeh

2020

Medical Imaging 2020: Computer-Aided Diagnosis

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Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

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Section: Resultsmentioning

confidence: 99%

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli

Nazar

Mehdizadeh

2020

Medical Imaging 2020: Computer-Aided Diagnosis

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“…Where TP is true positive, TN is true negative, FP is false positive and FN is false negative same as. 9,49,50 For all retinal images, our reader labeled the MAs on the images and the result of this manual segmentation are saved to be analyzed further. According to manual MAs detection by using the LoG vessel segmentation, our automated algorithm found sensitivity of more than 85% and specificity 80% in detection of MAs for 100 color images in MUMS-DB and 40 images of a DRIVE database.…”

Section: Resultsmentioning

confidence: 99%

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

Tavakoli¹,

Nazar²

2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found sensitivity of more than 80% in detection of MAs for all 140 images of two databases. And lastly, using Matched filter, our algorithm found sensitivity of more than 87% in detection of MAs in both local and DRIVE datasets. arXiv:2005.09097v1 [physics.med-ph] 18 Apr 2020

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“…Although each of the proposed methods has been somewhat successful, the attenuation problem is still not fully solved, and therefore, due to attenuation, the use of quantitative methods in SPECT is difficult. [ 14 15 23 ]…”

Section: Discussionmentioning

confidence: 99%

“…In addition, some radioactive substances emit several levels of gamma-ray energy and the amount of counting and the attenuation coefficient vary at each energy level. [ 23 24 25 ]…”

Section: Discussionmentioning

confidence: 99%

Attenuation correction in single-photon emission computed tomography for NURBS-based cardiac-torso phantom using dual-energy acquisition

et al. 2020

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Single photon emission tomography is widely used to detect photons emitted from the patient. Some of these emitted photons suffer from scattering and absorption because of the attenuation occurred through their path in patient's body. Therefore, the attenuation is the most important problem in single-photon emission computed tomography (SPECT) imaging. Some of the radioisotopes emit gamma rays in different energy levels, and consequently, they have different counts and attenuation coefficients. Calculation of the parameters used in the attenuation equation Nout=αNin= e− μ l Ninby mathematical methods is useful for the attenuation correction. Nurbs-based cardiac-torso (NCAT) phantom with an adequate attenuation coefficient and activity distribution is used in this study. Simulations were done using SimSET in 20—70 and 20—167 keV. A total of 128 projections were acquired over 360°. The corrected and reference images were compared using a universal image quality index (UIQI). The simulation repeated using NCAT phantom by SimSET. In the first group, no attenuation correction was used, but the Zubal coefficients were used for attenuation correction in the second image group. After the image reconstruction, a comparison between image groups was done using optimized UIQI to determine the quality of used reconstruction methods. Similarities of images were investigated by considering the average sinogram for every block size. The results showed that the proposed method improved the image quality. This study showed that simulation studies are useful tools in the investigation of nuclear medicine researches. We produced a nonattenuated model using Monte Carlo simulation method and compared it with an attenuated model. The proposed reconstruction method improved image resolution and contrast. Regional and general similarities of images could be determined, respectively, from acquired UIQI of small and large block sizes. Resulted curves from both small and large block sizes showed a good similarity between reconstructed and ideal images.

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Attenuation correction in SPECT images using attenuation map estimation with its emission data

Cited by 7 publications

References 0 publications

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

Attenuation correction in single-photon emission computed tomography for NURBS-based cardiac-torso phantom using dual-energy acquisition

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