The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hamidreza Saligheh Rad - One of the best experts on this subject based on the ideXlab platform.
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single ste mr acquisition in mr based attenuation correction of brain pet imaging employing a fully automated and reproducible level set Segmentation Approach
Molecular Imaging and Biology, 2017Co-Authors: Anahita Fathi Kazerooni, Saman Arfaie, Parisa Khateri, Hamidreza Saligheh RadAbstract:Purpose The aim of this study is to introduce a fully automatic and reproducible short echo-time (STE) magnetic resonance imaging (MRI) Segmentation Approach for MR-based attenuation correction of positron emission tomography (PET) data in head region.
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a fully automated and reproducible level set Segmentation Approach for generation of mr based attenuation correction map of pet images in the brain employing single ste mr imaging modality
EJNMMI Physics, 2014Co-Authors: Anahita Fathi Kazerooni, Mohammad Hadi Aarabi, Hamidreza Saligheh RadAbstract:Generating MR-based attenuation correction map (μ-map) for quantitative reconstruction of PET images still remains a challenge in hybrid PET/MRI systems, mainly because cortical bone structures are indistinguishable from proximal air cavities in conventional MR images. Recently, development of short echo-time (STE) MR imaging sequences, has shown promise in differentiating cortical bone from air. However, on STE-MR images, the bone appears with discontinuous boundaries. Therefore, Segmentation techniques based on intensity classification, such as thresholding or fuzzy C-means, fail to homogeneously delineate bone boundaries, especially in the presence of intrinsic noise and intensity inhomogeneity. Consequently, they cannot be fully automatized, must be fine-tuned on the case-by-case basis, and require additional morphological operations for Segmentation refinement. To overcome the mentioned problems, in this study, we introduce a new fully automatic and reproducible STE-MR Segmentation Approach exploiting level-set in a clustering-based intensity inhomogeneity correction framework to reliably delineate bone from soft tissue and air. MR images were acquired on a clinical 1.5T MRI System, MAGNETOM Avanto, using a FLASH 3D pulse sequence with TE=1.1ms, TR=12ms, flip angle=18°, voxel size=1.2×1.2×2mm3. For Segmentation of the STE-MR images into three regions, consisting of bone, air and soft tissue, a region-based level-set Segmentation algorithm was applied. In this technique, k-means clustering is applied to estimate the intensity properties of each region for bias field correction simultaneously with the level-set Segmentation. This algorithm incorporates both intensity and spatial information to define continuous boundaries. The quantitative assessment outcomes of the Segmentation performance yielded an average of 89%, 82%, 91%, and 73% for the accuracy, sensitivity, specificity and dice scores in bone Segmentation, respectively. The results suggest that the proposed fully automatic Segmentation Approach can reliably discriminate bony structures from the neighboring air and soft tissue in STE-MR images, which is suitable for generating accurate μ-maps in clinical PET/MR applications.
Anahita Fathi Kazerooni - One of the best experts on this subject based on the ideXlab platform.
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single ste mr acquisition in mr based attenuation correction of brain pet imaging employing a fully automated and reproducible level set Segmentation Approach
Molecular Imaging and Biology, 2017Co-Authors: Anahita Fathi Kazerooni, Saman Arfaie, Parisa Khateri, Hamidreza Saligheh RadAbstract:Purpose The aim of this study is to introduce a fully automatic and reproducible short echo-time (STE) magnetic resonance imaging (MRI) Segmentation Approach for MR-based attenuation correction of positron emission tomography (PET) data in head region.
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a fully automated and reproducible level set Segmentation Approach for generation of mr based attenuation correction map of pet images in the brain employing single ste mr imaging modality
EJNMMI Physics, 2014Co-Authors: Anahita Fathi Kazerooni, Mohammad Hadi Aarabi, Hamidreza Saligheh RadAbstract:Generating MR-based attenuation correction map (μ-map) for quantitative reconstruction of PET images still remains a challenge in hybrid PET/MRI systems, mainly because cortical bone structures are indistinguishable from proximal air cavities in conventional MR images. Recently, development of short echo-time (STE) MR imaging sequences, has shown promise in differentiating cortical bone from air. However, on STE-MR images, the bone appears with discontinuous boundaries. Therefore, Segmentation techniques based on intensity classification, such as thresholding or fuzzy C-means, fail to homogeneously delineate bone boundaries, especially in the presence of intrinsic noise and intensity inhomogeneity. Consequently, they cannot be fully automatized, must be fine-tuned on the case-by-case basis, and require additional morphological operations for Segmentation refinement. To overcome the mentioned problems, in this study, we introduce a new fully automatic and reproducible STE-MR Segmentation Approach exploiting level-set in a clustering-based intensity inhomogeneity correction framework to reliably delineate bone from soft tissue and air. MR images were acquired on a clinical 1.5T MRI System, MAGNETOM Avanto, using a FLASH 3D pulse sequence with TE=1.1ms, TR=12ms, flip angle=18°, voxel size=1.2×1.2×2mm3. For Segmentation of the STE-MR images into three regions, consisting of bone, air and soft tissue, a region-based level-set Segmentation algorithm was applied. In this technique, k-means clustering is applied to estimate the intensity properties of each region for bias field correction simultaneously with the level-set Segmentation. This algorithm incorporates both intensity and spatial information to define continuous boundaries. The quantitative assessment outcomes of the Segmentation performance yielded an average of 89%, 82%, 91%, and 73% for the accuracy, sensitivity, specificity and dice scores in bone Segmentation, respectively. The results suggest that the proposed fully automatic Segmentation Approach can reliably discriminate bony structures from the neighboring air and soft tissue in STE-MR images, which is suitable for generating accurate μ-maps in clinical PET/MR applications.
Jing Yuan - One of the best experts on this subject based on the ideXlab platform.
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automatic Segmentation Approach to extracting neonatal cerebral ventricles from 3d ultrasound images
Medical Image Analysis, 2017Co-Authors: Wu Qiu, Yimin Chen, Jessica Kishimoto, Sandrine De Ribaupierre, Bernard Chiu, Aaron Fenster, Jing YuanAbstract:Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated Segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic Segmentation Approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed Segmentation Approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented Approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic Segmentation of the ventricular system of premature neonatal brains from 3D US images.
Jude D Hemanth - One of the best experts on this subject based on the ideXlab platform.
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deep learning based enhanced tumor Segmentation Approach for mr brain images
Applied Soft Computing, 2019Co-Authors: Mamta Mittal, Lalit Mohan Goyal, Sumit Kaur, Iqbaldeep Kaur, Amit Verma, Jude D HemanthAbstract:Abstract Automation in medical industry has become one of the necessities in today’s medical scenario. Radiologists/physicians need such automation techniques for accurate diagnosis and treatment planning. Automatic Segmentation of tumor portion from Magnetic Resonance (MR) brain images is a challenging task. Several methodologies have been developed with an objective to enhance the Segmentation efficiency of the automated system. However, there is always scope for improvement in the Segmentation process of medical image analysis. In this work, deep learning-based Approach is proposed for brain tumor image Segmentation. The proposed method includes the concept of Stationary Wavelet Transform (SWT) and new Growing Convolution Neural Network (GCNN). The significant objective of this work is to enhance the accuracy of the conventional system. A comparative analysis with Support Vector Machine (SVM) and Convolution Neural Network (CNN) is carried out in this work. The experimental results prove that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.
Mohammad Hadi Aarabi - One of the best experts on this subject based on the ideXlab platform.
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a fully automated and reproducible level set Segmentation Approach for generation of mr based attenuation correction map of pet images in the brain employing single ste mr imaging modality
EJNMMI Physics, 2014Co-Authors: Anahita Fathi Kazerooni, Mohammad Hadi Aarabi, Hamidreza Saligheh RadAbstract:Generating MR-based attenuation correction map (μ-map) for quantitative reconstruction of PET images still remains a challenge in hybrid PET/MRI systems, mainly because cortical bone structures are indistinguishable from proximal air cavities in conventional MR images. Recently, development of short echo-time (STE) MR imaging sequences, has shown promise in differentiating cortical bone from air. However, on STE-MR images, the bone appears with discontinuous boundaries. Therefore, Segmentation techniques based on intensity classification, such as thresholding or fuzzy C-means, fail to homogeneously delineate bone boundaries, especially in the presence of intrinsic noise and intensity inhomogeneity. Consequently, they cannot be fully automatized, must be fine-tuned on the case-by-case basis, and require additional morphological operations for Segmentation refinement. To overcome the mentioned problems, in this study, we introduce a new fully automatic and reproducible STE-MR Segmentation Approach exploiting level-set in a clustering-based intensity inhomogeneity correction framework to reliably delineate bone from soft tissue and air. MR images were acquired on a clinical 1.5T MRI System, MAGNETOM Avanto, using a FLASH 3D pulse sequence with TE=1.1ms, TR=12ms, flip angle=18°, voxel size=1.2×1.2×2mm3. For Segmentation of the STE-MR images into three regions, consisting of bone, air and soft tissue, a region-based level-set Segmentation algorithm was applied. In this technique, k-means clustering is applied to estimate the intensity properties of each region for bias field correction simultaneously with the level-set Segmentation. This algorithm incorporates both intensity and spatial information to define continuous boundaries. The quantitative assessment outcomes of the Segmentation performance yielded an average of 89%, 82%, 91%, and 73% for the accuracy, sensitivity, specificity and dice scores in bone Segmentation, respectively. The results suggest that the proposed fully automatic Segmentation Approach can reliably discriminate bony structures from the neighboring air and soft tissue in STE-MR images, which is suitable for generating accurate μ-maps in clinical PET/MR applications.