The Experts below are selected from a list of 93456 Experts worldwide ranked by ideXlab platform
Huilin Xiong - One of the best experts on this subject based on the ideXlab platform.
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Ship Detection From PolSAR Imagery Using the Complete Polarimetric Covariance Difference Matrix
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Tao Zhang, Huilin XiongAbstract:In this paper, we proposed a complete polarimetric covariance Difference Matrix [ $CP$ ]-based algorithm for ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. To calculate [ $CP$ ], we first developed a scheme to reflect the polarimetric scattering Differences between ship pixel (SP) and its neighboring pixels (ISPs) and, then, dividedly accumulated the amplitude and phase Differences between SP and ISPs. Compared to the polarimetric covariance Difference Matrix [ $P$ ] developed in our earlier work, [ $CP$ ] effectively overcomes the drawback of the lack of the phase information in [ $P$ ]. To demonstrate the effectiveness of the proposed algorithm, we applied the [ $CP$ ]-based ship detection algorithm to four PolSAR data sets, including one UAVSAR L-band data set with 21 ships, two AIRSAR L-band data sets with 11 and 22 ships, respectively, and one Radarsat-2 C-band data set with 8 ships. Experimental results show that: 1) the proposed algorithm can effectively detect ships with high target-to-clutter ratio (TCR) values and 2) [ $CP$ ] has a better performance than the traditional polarimetric covariance Matrix [ $C$ ] and [ $P$ ] on ship detection. To be more specific, the average TCR value of the proposed algorithm (23.86 dB) is 6.07 and 7.47 dB higher than PNF $_{C}$ (i.e., the geometrical perturbation-polarimetric notch filter) and RS $_{C}$ (i.e., the reflection symmetry method), respectively.
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a ship detector based on the improved polarimetric covariance Difference Matrix
International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Tao Zhang, Yifang Ban, Huilin XiongAbstract:Polarimetric Synthetic Aperture Radar data has been widely used for ship detection. In our earlier study, based on the Differences between ship pixels and their surrounding background pixels, we designed a polarimetric covariance Difference Matrix (PCDM) to detect ships. Inadequately, the phase information of scattering Differences is not included in PCD-M. Aiming at this deficiency, here, we present an improved PCDM Matrix (IPCDM). Then an IPCDM-based ship detector is further proposed. To demonstrate the effectiveness of the method, two full polarimetric datasets are adopted. In comparing with other methods, we find that the result of our method is better.
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IGARSS - A Ship Detector Based on the Improved Polarimetric Covariance Difference Matrix
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Tao Zhang, Yifang Ban, Huilin XiongAbstract:Polarimetric Synthetic Aperture Radar data has been widely used for ship detection. In our earlier study, based on the Differences between ship pixels and their surrounding background pixels, we designed a polarimetric covariance Difference Matrix (PCDM) to detect ships. Inadequately, the phase information of scattering Differences is not included in PCD-M. Aiming at this deficiency, here, we present an improved PCDM Matrix (IPCDM). Then an IPCDM-based ship detector is further proposed. To demonstrate the effectiveness of the method, two full polarimetric datasets are adopted. In comparing with other methods, we find that the result of our method is better.
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PolSAR Ship Detection Based on the Polarimetric Covariance Difference Matrix
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Tao Zhang, Zhen Yang, Huilin XiongAbstract:Ship detection using Polarimetric SAR data has attracted a lot of attention in recent years. Due to the sampling of the Doppler spectrum at finite intervals of the pulse repetition frequency, the azimuth ambiguities often appear in PolSAR images, which make the ship detection in PolSAR images frequently generating false alarms, especially in the case of low backscattering sea environment. In order to handle the problem and improve the performance of ship detection in PolSAR images, this paper presents a new method, which is mainly based on concentrating the polarimetric Difference between ship pixels and background pixels. We first calculate a polarimetric covariance Difference Matrix, denoted as polarimetric covariance Difference Matrix (PCDM), by accumulating the elemental Difference between the polarimetric covariance Matrix at each pixel and the counterparts in its 3 × 3 neighbors. The SPAN detector is then applied on PCDM to obtain a coarse detection result. Meanwhile, we decompose the PCDM Matrix to calculate a new polarimetric signature, called pedestal ship height (PSH), and use it together with the coarse detection result to distinguish ships from ambiguities. Extensive experiments on three real PolSAR datasets are carried out to demonstrate the effectiveness of the proposed method in comparing with other algorithms. The experimental results show that the proposed method not only detects ships effectively, but also can remove the azimuth ambiguities and reduce the false alarms significantly.
R Jeraj - One of the best experts on this subject based on the ideXlab platform.
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using neighborhood gray tone Difference Matrix texture features on dual time point pet ct images to differentiate malignant from benign fdg avid solitary pulmonary nodules
Cancer Imaging, 2019Co-Authors: Song Chen, Stephanie Harmon, Timothy Perk, Meijie Chen, R JerajAbstract:Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone Difference Matrix (NGTDM) texture features. Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features. In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively. Compared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
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Using neighborhood gray tone Difference Matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules.
Cancer imaging : the official publication of the International Cancer Imaging Society, 2019Co-Authors: Song Chen, Stephanie Harmon, Timothy Perk, Meijie Chen, R JerajAbstract:Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone Difference Matrix (NGTDM) texture features. Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features. In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively. Compared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
Tao Zhang - One of the best experts on this subject based on the ideXlab platform.
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Ship Detection From PolSAR Imagery Using the Complete Polarimetric Covariance Difference Matrix
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Tao Zhang, Huilin XiongAbstract:In this paper, we proposed a complete polarimetric covariance Difference Matrix [ $CP$ ]-based algorithm for ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. To calculate [ $CP$ ], we first developed a scheme to reflect the polarimetric scattering Differences between ship pixel (SP) and its neighboring pixels (ISPs) and, then, dividedly accumulated the amplitude and phase Differences between SP and ISPs. Compared to the polarimetric covariance Difference Matrix [ $P$ ] developed in our earlier work, [ $CP$ ] effectively overcomes the drawback of the lack of the phase information in [ $P$ ]. To demonstrate the effectiveness of the proposed algorithm, we applied the [ $CP$ ]-based ship detection algorithm to four PolSAR data sets, including one UAVSAR L-band data set with 21 ships, two AIRSAR L-band data sets with 11 and 22 ships, respectively, and one Radarsat-2 C-band data set with 8 ships. Experimental results show that: 1) the proposed algorithm can effectively detect ships with high target-to-clutter ratio (TCR) values and 2) [ $CP$ ] has a better performance than the traditional polarimetric covariance Matrix [ $C$ ] and [ $P$ ] on ship detection. To be more specific, the average TCR value of the proposed algorithm (23.86 dB) is 6.07 and 7.47 dB higher than PNF $_{C}$ (i.e., the geometrical perturbation-polarimetric notch filter) and RS $_{C}$ (i.e., the reflection symmetry method), respectively.
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a ship detector based on the improved polarimetric covariance Difference Matrix
International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Tao Zhang, Yifang Ban, Huilin XiongAbstract:Polarimetric Synthetic Aperture Radar data has been widely used for ship detection. In our earlier study, based on the Differences between ship pixels and their surrounding background pixels, we designed a polarimetric covariance Difference Matrix (PCDM) to detect ships. Inadequately, the phase information of scattering Differences is not included in PCD-M. Aiming at this deficiency, here, we present an improved PCDM Matrix (IPCDM). Then an IPCDM-based ship detector is further proposed. To demonstrate the effectiveness of the method, two full polarimetric datasets are adopted. In comparing with other methods, we find that the result of our method is better.
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IGARSS - A Ship Detector Based on the Improved Polarimetric Covariance Difference Matrix
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Tao Zhang, Yifang Ban, Huilin XiongAbstract:Polarimetric Synthetic Aperture Radar data has been widely used for ship detection. In our earlier study, based on the Differences between ship pixels and their surrounding background pixels, we designed a polarimetric covariance Difference Matrix (PCDM) to detect ships. Inadequately, the phase information of scattering Differences is not included in PCD-M. Aiming at this deficiency, here, we present an improved PCDM Matrix (IPCDM). Then an IPCDM-based ship detector is further proposed. To demonstrate the effectiveness of the method, two full polarimetric datasets are adopted. In comparing with other methods, we find that the result of our method is better.
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PolSAR Ship Detection Based on the Polarimetric Covariance Difference Matrix
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Tao Zhang, Zhen Yang, Huilin XiongAbstract:Ship detection using Polarimetric SAR data has attracted a lot of attention in recent years. Due to the sampling of the Doppler spectrum at finite intervals of the pulse repetition frequency, the azimuth ambiguities often appear in PolSAR images, which make the ship detection in PolSAR images frequently generating false alarms, especially in the case of low backscattering sea environment. In order to handle the problem and improve the performance of ship detection in PolSAR images, this paper presents a new method, which is mainly based on concentrating the polarimetric Difference between ship pixels and background pixels. We first calculate a polarimetric covariance Difference Matrix, denoted as polarimetric covariance Difference Matrix (PCDM), by accumulating the elemental Difference between the polarimetric covariance Matrix at each pixel and the counterparts in its 3 × 3 neighbors. The SPAN detector is then applied on PCDM to obtain a coarse detection result. Meanwhile, we decompose the PCDM Matrix to calculate a new polarimetric signature, called pedestal ship height (PSH), and use it together with the coarse detection result to distinguish ships from ambiguities. Extensive experiments on three real PolSAR datasets are carried out to demonstrate the effectiveness of the proposed method in comparing with other algorithms. The experimental results show that the proposed method not only detects ships effectively, but also can remove the azimuth ambiguities and reduce the false alarms significantly.
Song Chen - One of the best experts on this subject based on the ideXlab platform.
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using neighborhood gray tone Difference Matrix texture features on dual time point pet ct images to differentiate malignant from benign fdg avid solitary pulmonary nodules
Cancer Imaging, 2019Co-Authors: Song Chen, Stephanie Harmon, Timothy Perk, Meijie Chen, R JerajAbstract:Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone Difference Matrix (NGTDM) texture features. Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features. In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively. Compared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
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Using neighborhood gray tone Difference Matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules.
Cancer imaging : the official publication of the International Cancer Imaging Society, 2019Co-Authors: Song Chen, Stephanie Harmon, Timothy Perk, Meijie Chen, R JerajAbstract:Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone Difference Matrix (NGTDM) texture features. Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features. In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively. Compared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
Dar-ren Chen - One of the best experts on this subject based on the ideXlab platform.
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3-D ultrasound texture classification using run Difference Matrix
Ultrasound in medicine & biology, 2005Co-Authors: Wei-ming Chen, Ruey-feng Chang, Shou Jen Kuo, Cheng Shyong Chang, Woo Kyung Moon, Shou Tung Chen, Dar-ren ChenAbstract:Abstract Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians’ offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run Difference Matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index A z under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively. (E-mail: dlchen88@ms13.hinet.net )
