The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Richard Turner - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Squamous Epithelium from Ultra-large Cervical Histological Virtual Slides
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Peter Hamilton, Richard TurnerAbstract:Cervical virtual slides are ultra-large, can have size up to 120 K times 80 K pixels. This paper introduces an image segmentation method for the automated identification of Squamous Epithelium from such virtual slides. In order to produce the best segmentation results, in addition to saving processing time and memory, a multiresolution segmentation strategy was developed. The Squamous Epithelium layer is first segmented at a low resolution (2X magnification). The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification, using an iterative boundary expanding-shrinking method. The block- based segmentation method uses robust texture feature vectors in combination with a Support Vector Machine (SVM) to perform classification. Medical histology rules are finally applied to remove misclassifications. Results demonstrate that, with typical virtual slides, classification accuracies of between 94.9% and 96.3% are achieved.
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Investigation of Methodologies for the Segmentation of Squamous Epithelium from Cervical Histological Virtual Slides
International Machine Vision and Image Processing Conference (IMVIP 2007), 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Richard Turner, Peter HamiltonAbstract:This paper investigates image segmentation methods for the automated identification of Squamous Epithelium from cervical virtual slides. Such images can be up to 120Ktimes80K pixels in size. Through investigation a multiresolution segmentation strategy was developed to give the best segmentation results in addition to saving processing time and memory. Squamous Epithelium is initially segmented at a low resolution of 2X magnification. The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification. Robust texture feature vectors were developed in conjunction with a support vector machine (SVM) to do classification. Finally medical histology rules are applied to remove misclassifications. Results show that with selected texture features, SVM achieved more than 92.1% accuracy in testing. In tests with 20 virtual slides, results are promising.
Yinhai Wang - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Squamous Epithelium from Ultra-large Cervical Histological Virtual Slides
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Peter Hamilton, Richard TurnerAbstract:Cervical virtual slides are ultra-large, can have size up to 120 K times 80 K pixels. This paper introduces an image segmentation method for the automated identification of Squamous Epithelium from such virtual slides. In order to produce the best segmentation results, in addition to saving processing time and memory, a multiresolution segmentation strategy was developed. The Squamous Epithelium layer is first segmented at a low resolution (2X magnification). The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification, using an iterative boundary expanding-shrinking method. The block- based segmentation method uses robust texture feature vectors in combination with a Support Vector Machine (SVM) to perform classification. Medical histology rules are finally applied to remove misclassifications. Results demonstrate that, with typical virtual slides, classification accuracies of between 94.9% and 96.3% are achieved.
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Investigation of Methodologies for the Segmentation of Squamous Epithelium from Cervical Histological Virtual Slides
International Machine Vision and Image Processing Conference (IMVIP 2007), 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Richard Turner, Peter HamiltonAbstract:This paper investigates image segmentation methods for the automated identification of Squamous Epithelium from cervical virtual slides. Such images can be up to 120Ktimes80K pixels in size. Through investigation a multiresolution segmentation strategy was developed to give the best segmentation results in addition to saving processing time and memory. Squamous Epithelium is initially segmented at a low resolution of 2X magnification. The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification. Robust texture feature vectors were developed in conjunction with a support vector machine (SVM) to do classification. Finally medical histology rules are applied to remove misclassifications. Results show that with selected texture features, SVM achieved more than 92.1% accuracy in testing. In tests with 20 virtual slides, results are promising.
Roy C Orlando - One of the best experts on this subject based on the ideXlab platform.
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electrical parameters and ion species for active transport in human esophageal stratified Squamous Epithelium and barrett s specialized columnar Epithelium
American Journal of Physiology-gastrointestinal and Liver Physiology, 2007Co-Authors: Nelia A Tobey, Chris M Argote, Ximena Vanegas, W Barlow, Roy C OrlandoAbstract:The human esophagus is lined by stratified Squamous Epithelium (ESSE), and in some subjects with reflux disease the distal esophagus becomes lined by Barrett's specialized columnar Epithelium (BSCE...
Peter Hamilton - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Squamous Epithelium from Ultra-large Cervical Histological Virtual Slides
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Peter Hamilton, Richard TurnerAbstract:Cervical virtual slides are ultra-large, can have size up to 120 K times 80 K pixels. This paper introduces an image segmentation method for the automated identification of Squamous Epithelium from such virtual slides. In order to produce the best segmentation results, in addition to saving processing time and memory, a multiresolution segmentation strategy was developed. The Squamous Epithelium layer is first segmented at a low resolution (2X magnification). The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification, using an iterative boundary expanding-shrinking method. The block- based segmentation method uses robust texture feature vectors in combination with a Support Vector Machine (SVM) to perform classification. Medical histology rules are finally applied to remove misclassifications. Results demonstrate that, with typical virtual slides, classification accuracies of between 94.9% and 96.3% are achieved.
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Investigation of Methodologies for the Segmentation of Squamous Epithelium from Cervical Histological Virtual Slides
International Machine Vision and Image Processing Conference (IMVIP 2007), 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Richard Turner, Peter HamiltonAbstract:This paper investigates image segmentation methods for the automated identification of Squamous Epithelium from cervical virtual slides. Such images can be up to 120Ktimes80K pixels in size. Through investigation a multiresolution segmentation strategy was developed to give the best segmentation results in addition to saving processing time and memory. Squamous Epithelium is initially segmented at a low resolution of 2X magnification. The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification. Robust texture feature vectors were developed in conjunction with a support vector machine (SVM) to do classification. Finally medical histology rules are applied to remove misclassifications. Results show that with selected texture features, SVM achieved more than 92.1% accuracy in testing. In tests with 20 virtual slides, results are promising.
Danny Crookes - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Squamous Epithelium from Ultra-large Cervical Histological Virtual Slides
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Peter Hamilton, Richard TurnerAbstract:Cervical virtual slides are ultra-large, can have size up to 120 K times 80 K pixels. This paper introduces an image segmentation method for the automated identification of Squamous Epithelium from such virtual slides. In order to produce the best segmentation results, in addition to saving processing time and memory, a multiresolution segmentation strategy was developed. The Squamous Epithelium layer is first segmented at a low resolution (2X magnification). The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification, using an iterative boundary expanding-shrinking method. The block- based segmentation method uses robust texture feature vectors in combination with a Support Vector Machine (SVM) to perform classification. Medical histology rules are finally applied to remove misclassifications. Results demonstrate that, with typical virtual slides, classification accuracies of between 94.9% and 96.3% are achieved.
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Investigation of Methodologies for the Segmentation of Squamous Epithelium from Cervical Histological Virtual Slides
International Machine Vision and Image Processing Conference (IMVIP 2007), 2007Co-Authors: Yinhai Wang, Danny Crookes, Jim Diamond, Richard Turner, Peter HamiltonAbstract:This paper investigates image segmentation methods for the automated identification of Squamous Epithelium from cervical virtual slides. Such images can be up to 120Ktimes80K pixels in size. Through investigation a multiresolution segmentation strategy was developed to give the best segmentation results in addition to saving processing time and memory. Squamous Epithelium is initially segmented at a low resolution of 2X magnification. The boundaries of segmented Squamous Epithelium are further fine tuned at the highest resolution of 40X magnification. Robust texture feature vectors were developed in conjunction with a support vector machine (SVM) to do classification. Finally medical histology rules are applied to remove misclassifications. Results show that with selected texture features, SVM achieved more than 92.1% accuracy in testing. In tests with 20 virtual slides, results are promising.
