The Experts below are selected from a list of 42753 Experts worldwide ranked by ideXlab platform
Mohammad Faizal Ahmad Fauzi - One of the best experts on this subject based on the ideXlab platform.
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On the Detection of Possible Landslides in Post-Event Satellite Images: A Probability Map Approach
International Journal of Machine Learning and Computing, 2015Co-Authors: Mohammad Faizal Ahmad Fauzi, Agustinus Deddy Arief Wibowo, Sin Liang Lim, Wooi Nee TanAbstract:Landslides are a significant hazard to property and livelihoods, causing millions of dollars worth of damage annually throughout the world, but especially in tropical regions such as Malaysia. Automated or semi-automated detection of landslides from aerial or satellite imagery and generating landslide susceptibility or hazard Map are two of the main research topics in landslide research. In this paper, we propose a Probability Map approach in detecting possible landslide regions from satellite or aerial images. The detected landslides, tabulated as landslide inventory Map, will be useful as the ground truth for evaluating landslide susceptibility Map, or even used as one of the causative factors for the susceptibility Map itself. The proposed Probability Map is computed using only colour information, but demonstrated very promising performance in locating potential landslide regions; thus provides a strong platform to locate actual landslides by incorporating texture and shape features in the future
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segmentation and automated measurement of chronic wound images Probability Map approach
Proceedings of SPIE, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue composition, measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) Probability Map, which then guides the region growing segmentation process. The red, yellow and black Probability Maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white Probability Map is designed to detect the white label card for measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific Probability Map for segmentation, 2) Computationally efficient regions growing on 4D Map; 3) Auto-calibration of measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.
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Medical Imaging: Computer-Aided Diagnosis - Segmentation and automated measurement of chronic wound images: Probability Map approach
Medical Imaging 2014: Computer-Aided Diagnosis, 2014Co-Authors: Mohammad Faizal Ahmad Fauzi, Ibrahim Khansa, Karen Catignani, Gayle M Gordillo, Chandan K Sen, Metin N GurcanAbstract:estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue composition, measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra- and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) Probability Map, which then guides the region growing segmentation process. The red, yellow and black Probability Maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white Probability Map is designed to detect the white label card for measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific Probability Map for segmentation, 2) Computationally efficient regions growing on 4D Map; 3) Auto-calibration of measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.
Mark Campbell - One of the best experts on this subject based on the ideXlab platform.
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Probability Map building algorithms design for an unknown dynamic environment
Intelligent Robots and Systems, 2006Co-Authors: Yongchun Fang, Mark CampbellAbstract:In this paper, we consider the problem of building a Probability Map for an unknown hostile environment by utilizing a team of UAVs. Specifically, we first present a centralized Map building scheme for the Boeing Open Experimental Platform (OEP) environment, the strategy is then modified into a decentralized Map building algorithm to increase the robustness of the system. Some simulation results are provided to demonstrate the validity of the proposed algorithms.
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IROS - Probability Map Building Algorithms Design for an Unknown Dynamic Environment
2006 IEEE RSJ International Conference on Intelligent Robots and Systems, 2006Co-Authors: Yongchun Fang, Mark CampbellAbstract:In this paper, we consider the problem of building a Probability Map for an unknown hostile environment by utilizing a team of UAVs. Specifically, we first present a centralized Map building scheme for the Boeing Open Experimental Platform (OEP) environment, the strategy is then modified into a decentralized Map building algorithm to increase the robustness of the system. Some simulation results are provided to demonstrate the validity of the proposed algorithms.
Stephen T C Wong - One of the best experts on this subject based on the ideXlab platform.
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tissue Probability Map constrained 4d clustering algorithm for increased accuracy and robustness in serialmr brain image segmentation
International Journal of Medical Engineering and Informatics, 2011Co-Authors: Zhong Xue, Dinggang Shen, Stephen T C WongAbstract:The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue Probability Map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue Probability Maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders.
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tissue Probability Map constrained classic for increased accuracy and robustness in serial image segmentation
Proceedings of SPIE, 2009Co-Authors: Zhong Xue, Dinggang Shen, Stephen T C WongAbstract:Traditional fuzzy clustering algorithms have been successfully applied in MR image segmentation for quantitative morphological analysis. However, the clustering results might be biased due to the variability of tissue intensities and anatomical structures. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue Probability Map constrained clustering algorithm and apply it to serialMR brain image segmentation for longitudinal study of human brains. The tissue Probability Maps consist of segmentation priors obtained from a population and reflect the Probability of different tissue types. More accurate image segmentation can be achieved by using these segmentation priors in the clustering algorithm. Experimental results of both simulated longitudinal MR brain data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using the new serial image segmentation algorithm in the framework of CLASSIC show more accurate and robust longitudinal measures.
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Medical Imaging: Image Processing - Tissue Probability Map constrained CLASSIC for increased accuracy and robustness in serial image segmentation
Medical Imaging 2009: Image Processing, 2009Co-Authors: Zhong Xue, Dinggang Shen, Stephen T C WongAbstract:Traditional fuzzy clustering algorithms have been successfully applied in MR image segmentation for quantitative morphological analysis. However, the clustering results might be biased due to the variability of tissue intensities and anatomical structures. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue Probability Map constrained clustering algorithm and apply it to serialMR brain image segmentation for longitudinal study of human brains. The tissue Probability Maps consist of segmentation priors obtained from a population and reflect the Probability of different tissue types. More accurate image segmentation can be achieved by using these segmentation priors in the clustering algorithm. Experimental results of both simulated longitudinal MR brain data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using the new serial image segmentation algorithm in the framework of CLASSIC show more accurate and robust longitudinal measures.
Dorin Comaniciu - One of the best experts on this subject based on the ideXlab platform.
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model based esophagus segmentation from ct scans using a spatial Probability Map
Medical Image Computing and Computer-Assisted Intervention, 2010Co-Authors: Johannes Feulner, Kevin S Zhou, Martin Huber, Alexander Cavallaro, Joachim Hornegger, Dorin ComaniciuAbstract:Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial Probability Map generated from detected air. Threefold cross-validation on 144 datasets showed that this Probability Map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.
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MICCAI (1) - Model-based esophagus segmentation from CT scans using a spatial Probability Map
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2010Co-Authors: Johannes Feulner, Martin Huber, Alexander Cavallaro, Joachim Hornegger, S. Kevin Zhou, Dorin ComaniciuAbstract:Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial Probability Map generated from detected air. Threefold cross-validation on 144 datasets showed that this Probability Map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.
Yongchun Fang - One of the best experts on this subject based on the ideXlab platform.
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Probability Map building algorithms design for an unknown dynamic environment
Intelligent Robots and Systems, 2006Co-Authors: Yongchun Fang, Mark CampbellAbstract:In this paper, we consider the problem of building a Probability Map for an unknown hostile environment by utilizing a team of UAVs. Specifically, we first present a centralized Map building scheme for the Boeing Open Experimental Platform (OEP) environment, the strategy is then modified into a decentralized Map building algorithm to increase the robustness of the system. Some simulation results are provided to demonstrate the validity of the proposed algorithms.
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IROS - Probability Map Building Algorithms Design for an Unknown Dynamic Environment
2006 IEEE RSJ International Conference on Intelligent Robots and Systems, 2006Co-Authors: Yongchun Fang, Mark CampbellAbstract:In this paper, we consider the problem of building a Probability Map for an unknown hostile environment by utilizing a team of UAVs. Specifically, we first present a centralized Map building scheme for the Boeing Open Experimental Platform (OEP) environment, the strategy is then modified into a decentralized Map building algorithm to increase the robustness of the system. Some simulation results are provided to demonstrate the validity of the proposed algorithms.
