Joint Histogram

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Simon Parsons - One of the best experts on this subject based on the ideXlab platform.

  • An analysis of the cloud environment over the Ross Sea and Ross Ice Shelf using CloudSat/CALIPSO satellite observations: the importance of synoptic forcing
    'Copernicus GmbH', 2018
    Co-Authors: Ben Jolly, Peter Kuma, A. Mcdonald, Simon Parsons
    Abstract:

    We use the 2B-GEOPROF-LIDAR R04 (2BGL4) and R05 (2BGL5) products and the 2B-CLDCLASS-LIDAR R04 (2BCL4) product, all generated by combining CloudSat radar and CALIPSO lidar satellite measurements with auxiliary data, to examine the vertical distribution of cloud occurrence around the Ross Ice Shelf (RIS) and Ross Sea region. We find that the 2BGL4 product, used in previous studies in this region, displays a discontinuity at 8.2 km which is not observable in the other products. This artefact appears to correspond to a change in the horizontal and vertical resolution of the CALIPSO dataset used above this level. We then use the 2BCL4 product to examine the vertical distribution of cloud occurrence, phase, and type over the RIS and Ross Sea. In particular we examine how synoptic conditions in the region, derived using a previously developed synoptic classification, impact the cloud environment and the contrasting response in the two regions. We observe large differences between the cloud occurrence as a function of altitude for synoptic regimes relative to those for seasonal variations. A stronger variation in the occurrence of clear skies and multi-layer cloud and in all cloud type occurrences over both the Ross Sea and RIS is associated more with synoptic type than seasonal composites. In addition, anomalies from the mean Joint Histogram of cloud top height against thickness display significant differences over the Ross Sea and RIS sectors as a function of synoptic regime, but are near identical over these two regions when a seasonal analysis is completed. However, the frequency of particular phases of cloud, notably mixed phase and water, is much more strongly modulated by seasonal than synoptic regime compositing, which suggests that temperature is still the most important control on cloud phase in the region.

  • an analysis of the cloud environment over the ross sea and ross ice shelf using cloudsat calipso satellite observations the importance of synoptic forcing
    Atmospheric Chemistry and Physics, 2017
    Co-Authors: Ben Jolly, A J Mcdonald, Peter Kuma, Simon Parsons
    Abstract:

    Abstract. We use the 2B-GEOPROF-LIDAR R04 (2BGL4) and R05 (2BGL5) products and the 2B-CLDCLASS-LIDAR R04 (2BCL4) product, all generated by combining CloudSat radar and CALIPSO lidar satellite measurements with auxiliary data, to examine the vertical distribution of cloud occurrence around the Ross Ice Shelf (RIS) and Ross Sea region. We find that the 2BGL4 product, used in previous studies in this region, displays a discontinuity at 8.2  km which is not observable in the other products. This artefact appears to correspond to a change in the horizontal and vertical resolution of the CALIPSO dataset used above this level. We then use the 2BCL4 product to examine the vertical distribution of cloud occurrence, phase, and type over the RIS and Ross Sea. In particular we examine how synoptic conditions in the region, derived using a previously developed synoptic classification, impact the cloud environment and the contrasting response in the two regions. We observe large differences between the cloud occurrence as a function of altitude for synoptic regimes relative to those for seasonal variations. A stronger variation in the occurrence of clear skies and multi-layer cloud and in all cloud type occurrences over both the Ross Sea and RIS is associated more with synoptic type than seasonal composites. In addition, anomalies from the mean Joint Histogram of cloud top height against thickness display significant differences over the Ross Sea and RIS sectors as a function of synoptic regime, but are near identical over these two regions when a seasonal analysis is completed. However, the frequency of particular phases of cloud, notably mixed phase and water, is much more strongly modulated by seasonal than synoptic regime compositing, which suggests that temperature is still the most important control on cloud phase in the region.

Hua-mei Chen - One of the best experts on this subject based on the ideXlab platform.

  • comparison and evaluation of Joint Histogram estimation methods for mutual information based image registration
    Medical Imaging 2005: Image Processing, 2005
    Co-Authors: Yongfang Liang, Hua-mei Chen
    Abstract:

    Joint Histogram is the only quantity required to calculate the mutual information (MI) between two images. For MI based image registration, Joint Histograms are often estimated through linear interpolation or partial volume interpolation (PVI). It has been pointed out that both methods may result in a phenomenon known as interpolation induced artifacts. In this paper, we implemented a wide range of interpolation/approximation kernels for Joint Histogram estimation. Some kernels are nonnegative. In this case, these kernels are applied in two ways as the linear kernel is applied in linear interpolation and PVI. In addition, we implemented two other Joint Histogram estimation methods devised to overcome the interpolation artifact problem. They are nearest neighbor interpolation with jittered sampling with/without Histogram blurring and data resampling. We used the clinical data obtained from Vanderbilt University for all of the experiments. The objective of this study is to perform a comprehensive comparison and evaluation of different Joint Histogram estimation methods for MI based image registration in terms of artifacts reduction and registration accuracy.

  • performance of mutual information similarity measure for registration of multitemporal remote sensing images
    IEEE Transactions on Geoscience and Remote Sensing, 2003
    Co-Authors: Hua-mei Chen, Pramod K. Varshney, Manoj K. Arora
    Abstract:

    Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of Joint Histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new Joint Histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual-information-based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures, namely, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images.

  • Mutual information-based CT-MR brain image registration using generalized partial volume Joint Histogram estimation
    IEEE Transactions on Medical Imaging, 2003
    Co-Authors: Hua-mei Chen, P.k. Varshney
    Abstract:

    Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the Joint Histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the Joint Histogram for registration purposes. It has been shown that Joint Histogram estimation through these two interpolation methods may introduce artifacts in the MI registration function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new Joint Histogram estimation scheme called generalized partial volume estimation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.

  • A study of Joint Histogram estimation methods to register multi-sensor remote sensing images using mutual information
    IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2024
    Co-Authors: Hua-mei Chen, Pramod K. Varshney, Manoj K. Arora
    Abstract:

    Registration is the basic image processing operation in a variety of tasks such as multi-source classification, image fusion and change detection. Automatic intensity based registration techniques are gaining importance. In this paper, we investigate an intensity based technique that utilizes mutual information as the similarity measure. We apply this technique to perform multi-sensor image registration. The performance of a number of Joint Histogram estimation methods for the determination of mutual information has been evaluated using a measure called registration consistency. These methods include partial volume interpolation, cubic convolution interpolation, linear interpolation, and nearest neighborhood interpolation. The results show that partial volume interpolation produces the most reliable registration consistency. Nearest neighbor interpolation outperforms linear and cubic convolution interpolation.

Mark R Pickering - One of the best experts on this subject based on the ideXlab platform.

  • a non rigid 3d multi modal registration algorithm using partial volume interpolation and the sum of conditional variance
    Digital Image Computing: Techniques and Applications, 2014
    Co-Authors: Mst Nargis Aktar, Md Jahangir Alam, Mark R Pickering
    Abstract:

    Multi-modal medical image registration provides complementary information from the fusion of various medical imaging modalities. This paper presents a volume based multi-modal affine registration algorithm to register images acquired using different magnetic resonance imaging (MRI) modes. In the proposed algorithm, the sum-of-conditional variance (SCV) similarity measure is used. The SCV is considered to be a state-of-the- art similarity measure for registering multi-modal images. However, the main drawback of the SCV is that it uses only quantized information to calculate a Joint Histogram. To overcome this limitation, we propose to use partial volume interpolation (PVI) in the Joint Histogram calculation to improve the performance of the existing registration algorithm. To evaluate the performance of the registration algorithm, different similarity measures were compared in conjunction with gradient-based Gauss-Newton(GN) optimization to optimize the spatial transformation parameters. The experimental evaluation shows that the proposed approach provides a higher success rate and comparable accuracy to other methods that have been recently proposed for multi-modal medical image registration.

  • robust 3d multi modal registration of mri volumes using the sum of conditional variance
    Digital Image Computing: Techniques and Applications, 2013
    Co-Authors: Nargis Aktar, Jahangir Alam, Andrew Lambert, Mark R Pickering
    Abstract:

    Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of conditional variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of conditional variances that are calculated using the Joint Histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.

Paul Suetens - One of the best experts on this subject based on the ideXlab platform.

  • nonrigid image registration using conditional mutual information
    Information Processing in Medical Imaging, 2007
    Co-Authors: Dirk Loeckx, Pieter Slagmolen, Frederik Maes, Dirk Vandermeulen, Paul Suetens
    Abstract:

    We propose conditional mutual information (cMI) as a new similarity measure for nonrigid image registration. We start from a 3D Joint Histogram incorporating, besides the reference and floating intensity dimensions, also a spatial dimension expressing the location of the Joint intensity pair in the reference image. cMI is calculated as the expectation value of the conditional mutual information between the reference and floating intensities given the spatial distribution. Validation experiments were performed comparing cMI and global MI on artificial CT/MR registrations and registrations complicated with a strong bias field; both a Parzen window and generalised partial volume kernel were used for Histogram construction. In both experiments, cMI significantly outperforms global MI. Moreover, cMI is compared to global MI for the registration of three patient CT/MR datasets, using overlap and centroid distance as validation measure. The best results are obtained using cMI.

  • nonrigid image registration using conditional mutual information
    Lecture Notes in Computer Science, 2007
    Co-Authors: Dirk Loeckx, Pieter Slagmolen, Frederik Maes, Dirk Vandermeulen, Paul Suetens
    Abstract:

    Maximization of mutual information (MMI) is a popular similarity measure for medical image registration. Although its accuracy and robustness has been demonstrated for rigid body image registration, extending MMI to nonrigid image registration is not trivial and an active field of research. We propose conditional mutual information (cMI) as a new similarity measure for nonrigid image registration. cMI starts from a 3-D Joint Histogram incorporating, besides the intensity dimensions, also a spatial dimension expressing the location of the Joint intensity pair. cMI is calculated as the expected value of the cMI between the image intensities given the spatial distribution. The cMI measure was incorporated in a tensor-product B-spline nonrigid registration method, using either a Parzen window or generalized partial volume kernel for Histogram construction. cMI was compared to the classical global mutual information (gMI) approach in theoretical, phantom, and clinical settings. We show that cMI significantly outperforms gMI for all applications.

Jiunlin Yan - One of the best experts on this subject based on the ideXlab platform.

  • intratumoral heterogeneity of glioblastoma infiltration revealed by Joint Histogram analysis of diffusion tensor imaging
    Neurosurgery, 2019
    Co-Authors: Shuo Wang, Jiunlin Yan, Rory J Piper, Hongxiang Liu, Turid Torheim, Hyunjin Kim, Jingjing Zou
    Abstract:

    BACKGROUND Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption. OBJECTIVE To explore the heterogeneity of glioblastoma infiltration using Joint Histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy. METHODS A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The Joint Histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with Joint Histogram features and relevant clinical factors. The incremental prognostic values of Histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation. RESULTS We found that Joint Histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models. CONCLUSION Joint Histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.

  • intratumoral heterogeneity of tumor infiltration of glioblastoma revealed by Joint Histogram analysis of diffusion tensor imaging
    bioRxiv, 2017
    Co-Authors: Shuo Wang, Jiunlin Yan, Rory J Piper, Hongxiang Liu, Turid Torheim, Hyunjin Kim, Natalie R Boonzaier, Rohitashwa Sinha, Tomasz Matys, Florian Markowetz
    Abstract:

    Purpose: To solve the challenge of interpreting diffusion tensor imaging (DTI), we proposed a Joint Histogram analysis of the isotropic (p) and anisotropic (q) components of DTI. We explored the heterogeneity of glioblastoma infiltration using the Joint Histogram features and evaluated their prognostic values. Materials and methods: A total of 115 primary glioblastoma patients were prospectively recruited and preoperatively imaged. Patients underwent maximal safe resection. DTI was processed and decomposed into p and q components. Pixel values were extracted from DTI-p and -q maps and used to construct the univariate and Joint Histograms, in contrast-enhancing and non-enhancing regions respectively. Eight Joint Histogram features were obtained and then correlated with patient survival and tumor progression rate. Their prognostic values were examined and compared with clinical factors using receiver operating characteristic curves. Results: Both univariate and Joint Histogram showed that the subregion of increased DTI-p and decreased DTI-q accounted for the largest proportion. However, additional diffusion patterns can be identified via Joint Histogram analysis. Particularly, a higher proportion of decreased DTI-p and increased DTI-q in non-enhancing region contributed to worse progression-free survival and worse overall survival (both HR = 1.12, p

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