The Experts below are selected from a list of 22869 Experts worldwide ranked by ideXlab platform
Nasir M Rajpoot - One of the best experts on this subject based on the ideXlab platform.
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fast and accurate tumor segmentation of histology Images using persistent homology and deep convolutional features
Medical Image Analysis, 2019Co-Authors: Talha Qaiser, Yeewah Tsang, Daiki Taniyama, Naoya Sakamoto, Kazuaki Nakane, D B A Epstein, Nasir M RajpootAbstract:Abstract Tumor segmentation in whole-slide Images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given Image Patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an Image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on a selection of exemplar Image Patches from a convolution neural network (CNN) and Patch classification by quantifying the divergence between the PHPs of exemplars and the input Image Patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for Image Patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperform competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet, and healthy cases. Collectively, the accurate tumor segmentation produces the highest average Patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology Image analysis.
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fast and accurate tumor segmentation of histology Images using persistent homology and deep convolutional features
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Talha Qaiser, Yeewah Tsang, Daiki Taniyama, Naoya Sakamoto, Kazuaki Nakane, D B A Epstein, Nasir M RajpootAbstract:Tumor segmentation in whole-slide Images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given Image Patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an Image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on the selection of exemplar Image Patches from a convolution neural network (CNN) and Patch classification by quantifying the divergence between the PHPs of exemplars and the input Image Patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for Image Patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperforms competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor segmentation produces the highest average Patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology Image analysis.
Dagan Feng - One of the best experts on this subject based on the ideXlab platform.
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lung nodule classification with multilevel Patch based context analysis
IEEE Transactions on Biomedical Engineering, 2014Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Minzhao Lee, Heng Huang, Michael J Fulham, Dagan FengAbstract:In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive Patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for Image Patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant Images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.
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context curves for classification of lung nodule Images
Digital Image Computing: Techniques and Applications, 2013Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Dagan FengAbstract:In this paper, a feature-based imaging classification method is presented to classify the lung nodules in low dose computed tomography (LDCT) slides into four categories: well-circumscribed, vascularized, juxta-pleural and pleural-tail. The proposed method focuses on the feature design, which describes both lung nodule and surrounding context information, and contains two main stages: (1) superpixel labeling, which labels the pixels into foreground and background based on an Image Patch division approach, (2) context curve calculation, which transfers the superpixel labeling result into feature vector. While the first stage preprocesses the Image, extracting the major context anatomical structures for each type of nodules, the context curve provides a discriminative description for intra- and inter-type nodules. The evaluation is conducted on a publicly available dataset and the results indicate the promising performance of the proposed method on lung nodule classification.
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feature based Image Patch approximation for lung tissue classification
IEEE Transactions on Medical Imaging, 2013Co-Authors: Yang Song, Yun Zhou, Weidong Cai, Dagan FengAbstract:In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) Images, with feature-based Image Patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each Image Patch is then labeled based on its feature approximation from reference Image Patches. And a new Patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, Patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The Patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
Alfred O Hero - One of the best experts on this subject based on the ideXlab platform.
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Image Patch analysis of sunspots and active regions ii clustering via matrix factorization
Journal of Space Weather and Space Climate, 2016Co-Authors: Kevin R Moon, Veronique Delouille, Fraser Watson, Ruben De Visscher, Alfred O HeroAbstract:Context . Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region’s evolution for example. Aims . We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum Images. Methods . We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local Image Patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. Results . We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the R -value. Conclusions . Matrix factorization of Image Patches is a promising new way of characterizing active regions. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.
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Image Patch analysis of sunspots and active regions i intrinsic dimension and correlation analysis
Journal of Space Weather and Space Climate, 2016Co-Authors: Kevin R Moon, Veronique Delouille, Fraser Watson, Ruben De Visscher, Alfred O HeroAbstract:Context. The flare productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a categorical way, and may therefore not use all the information present in the observations. Moreover, such categorical schemes hinder a systematic study of an active region’s evolution for example. Aims . We propose fine-scale quantitative descriptors for an active region’s complexity and relate them to the Mount Wilson classification. We analyze the local correlation structure within continuum and magnetogram data, as well as the cross-correlation between continuum and magnetogram data. Methods . We compute the intrinsic dimension, partial correlation, and canonical correlation analysis (CCA) of Image Patches of continuum and magnetogram active region Images taken from the SOHO-MDI instrument. We use masks of sunspots derived from continuum as well as larger masks of magnetic active regions derived from magnetogram to analyze separately the core part of an active region from its surrounding part. Results . We find relationships between the complexity of an active region as measured by its Mount Wilson classification and the intrinsic dimension of its Image Patches. Partial correlation patterns exhibit approximately a third-order Markov structure. CCA reveals different patterns of correlation between continuum and magnetogram within the sunspots and in the region surrounding the sunspots. Conclusions . Intrinsic dimension has the potential to distinguish simple from complex active regions. These results also pave the way for Patch-based dictionary learning with a view toward automatic clustering of active regions.
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Image Patch analysis of sunspots and active regions ii clustering via matrix factorization
arXiv: Solar and Stellar Astrophysics, 2015Co-Authors: Kevin R Moon, Veronique Delouille, Fraser Watson, Ruben De Visscher, Alfred O HeroAbstract:Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region's evolution for example. We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum Images. We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local Image Patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the $R$ value. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.
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Image Patch analysis of sunspots and active regions i intrinsic dimension and correlation analysis
arXiv: Solar and Stellar Astrophysics, 2015Co-Authors: Kevin R Moon, Veronique Delouille, Fraser Watson, Ruben De Visscher, Alfred O HeroAbstract:The flare-productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a categorical way, and may therefore not use all the information present in the observations. Moreover, such categorical schemes hinder a systematic study of an active region's evolution for example. We propose fine-scale quantitative descriptors for an active region's complexity and relate them to the Mount Wilson classification. We analyze the local correlation structure within continuum and magnetogram data, as well as the cross-correlation between continuum and magnetogram data. We compute the intrinsic dimension, partial correlation, and canonical correlation analysis (CCA) of Image Patches of continuum and magnetogram active region Images taken from the SOHO-MDI instrument. We use masks of sunspots derived from continuum as well as larger masks of magnetic active regions derived from the magnetogram to analyze separately the core part of an active region from its surrounding part. We find the relationship between complexity of an active region as measured by Mount Wilson and the intrinsic dimension of its Image Patches. Partial correlation patterns exhibit approximately a third-order Markov structure. CCA reveals different patterns of correlation between continuum and magnetogram within the sunspots and in the region surrounding the sunspots. These results also pave the way for Patch-based dictionary learning with a view towards automatic clustering of active regions.
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Image Patch analysis and clustering of sunspots a dimensionality reduction approach
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: Kevin R Moon, Veronique Delouille, Fraser Watson, Alfred O HeroAbstract:Sunspots, as seen in white light or continuum Images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram Images. Their complexity is correlated with explosive solar activity and so classifying these active regions is useful for predicting future solar activity. Current classification of sunspot groups is visually based and suffers from bias. Supervised learning methods can reduce human bias but fail to optimally capitalize on the information present in sunspot Images. This paper uses two Image modalities (continuum and magnetogram) to characterize the spatial and modal interactions of sunspot and magnetic active region Images and presents a new approach to cluster the Images. Specifically, in the framework of Image Patch analysis, we estimate the number of intrinsic parameters required to describe the spatial and modal dependencies, the correlation between the two modalities and the corresponding spatial patterns, and examine the phenomena at different scales within the Images. To do this, we use linear and nonlinear intrinsic dimension estimators, canonical correlation analysis, and multiresolution analysis of intrinsic dimension.
Yun Zhou - One of the best experts on this subject based on the ideXlab platform.
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lung nodule classification with multilevel Patch based context analysis
IEEE Transactions on Biomedical Engineering, 2014Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Minzhao Lee, Heng Huang, Michael J Fulham, Dagan FengAbstract:In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive Patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for Image Patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant Images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.
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medical Image classification with convolutional neural network
International Conference on Control Automation Robotics and Vision, 2014Co-Authors: Xiaogang Wang, David Dagan Feng, Yun Zhou, Mei ChenAbstract:Image Patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung Image Patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic Image features from lung Image Patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical Image or texture classification tasks.
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context curves for classification of lung nodule Images
Digital Image Computing: Techniques and Applications, 2013Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Dagan FengAbstract:In this paper, a feature-based imaging classification method is presented to classify the lung nodules in low dose computed tomography (LDCT) slides into four categories: well-circumscribed, vascularized, juxta-pleural and pleural-tail. The proposed method focuses on the feature design, which describes both lung nodule and surrounding context information, and contains two main stages: (1) superpixel labeling, which labels the pixels into foreground and background based on an Image Patch division approach, (2) context curve calculation, which transfers the superpixel labeling result into feature vector. While the first stage preprocesses the Image, extracting the major context anatomical structures for each type of nodules, the context curve provides a discriminative description for intra- and inter-type nodules. The evaluation is conducted on a publicly available dataset and the results indicate the promising performance of the proposed method on lung nodule classification.
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feature based Image Patch approximation for lung tissue classification
IEEE Transactions on Medical Imaging, 2013Co-Authors: Yang Song, Yun Zhou, Weidong Cai, Dagan FengAbstract:In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) Images, with feature-based Image Patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each Image Patch is then labeled based on its feature approximation from reference Image Patches. And a new Patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, Patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The Patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
Yang Song - One of the best experts on this subject based on the ideXlab platform.
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lung nodule classification with multilevel Patch based context analysis
IEEE Transactions on Biomedical Engineering, 2014Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Minzhao Lee, Heng Huang, Michael J Fulham, Dagan FengAbstract:In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive Patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for Image Patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant Images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.
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context curves for classification of lung nodule Images
Digital Image Computing: Techniques and Applications, 2013Co-Authors: Fan Zhang, Yun Zhou, Yang Song, Weidong Cai, Shimin Shan, Dagan FengAbstract:In this paper, a feature-based imaging classification method is presented to classify the lung nodules in low dose computed tomography (LDCT) slides into four categories: well-circumscribed, vascularized, juxta-pleural and pleural-tail. The proposed method focuses on the feature design, which describes both lung nodule and surrounding context information, and contains two main stages: (1) superpixel labeling, which labels the pixels into foreground and background based on an Image Patch division approach, (2) context curve calculation, which transfers the superpixel labeling result into feature vector. While the first stage preprocesses the Image, extracting the major context anatomical structures for each type of nodules, the context curve provides a discriminative description for intra- and inter-type nodules. The evaluation is conducted on a publicly available dataset and the results indicate the promising performance of the proposed method on lung nodule classification.
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feature based Image Patch approximation for lung tissue classification
IEEE Transactions on Medical Imaging, 2013Co-Authors: Yang Song, Yun Zhou, Weidong Cai, Dagan FengAbstract:In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) Images, with feature-based Image Patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each Image Patch is then labeled based on its feature approximation from reference Image Patches. And a new Patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, Patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The Patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
