Image Similarity

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Robert Laganiere - One of the best experts on this subject based on the ideXlab platform.

  • phase congruence measurement for Image Similarity assessment
    Pattern Recognition Letters, 2007
    Co-Authors: Zheng Liu, Robert Laganiere
    Abstract:

    In the performance assessment of an Image processing algorithm, an Image is often compared with an available reference. Measuring Image Similarity can be achieved in many ways; comparison algorithm varies from pixel-based mean square error method to structure-based Image quality index. In this paper, we present a new feature-based approach that utilizes Image phase congruency measurement to quantify the assessment of the similarities or differences between two Images. Test results with standard Images and industrial inspection Images are presented.

  • ICASSP (2) - On the Use of Phase Congruency to Evaluate Image Similarity
    2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1
    Co-Authors: Zheng Liu, Robert Laganiere
    Abstract:

    Measuring Image Similarity is important in many applications. Different algorithms propose to compare Images using pixel-based mean square error methods others use structure-based Image quality index. We present, here, a new feature-based approach that utilizes Image phase congruency measurement to quantify the assessment of the similarities or differences between two Images

Hiroshi Natori - One of the best experts on this subject based on the ideXlab platform.

  • Selective Image Similarity measure for bronchoscope tracking based on Image registration.
    Medical image analysis, 2009
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Marco Feuerstein, Takayuki Kitasaka, Calvin R. Maurer, Yasuhito Suenaga, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • New Image Similarity measures for bronchoscope tracking based on Image registration between virtual and real bronchoscopic Images
    Medical Imaging 2004: Physiology Function and Structure from Medical Images, 2004
    Co-Authors: Kensaku Mori, Daisuke Deguchi, Takayuki Kitasaka, Yasuhito Suenaga, Hirotsugu Takabatake, Jun-ichiro Toriwaki, Tsutomu Enjoji, Hiroshi Natori
    Abstract:

    This paper describes a new method for calculating Image Similarity between a real bronchoscopic (RB) Image and a virtual endoscopic (VE) Image for bronchoscope tracking based on Image registration. Camera motion tracking is sequentially done by finding viewing parameters (camera position and orientation) that can render the most similar VE Image to a currently processing RB frame based on Image Similarity, since it is difficult to attach a positional sensor at the tip of a bronchoscope. In the previous method, Image Similarity was calculated between real and virtual endoscopic Images by summing gray-level differences up for all pixels of two Images. This method could not estimate positions and orientations of a real bronchoscope camera properly, when Image Similarity changed only a little (but partly changed significantly) due to averaging of gray-level differences for the entire Image. The proposed method divides the real and virtual endoscopic Images into a set of subregions and selects the subregions that contain characteristic shapes such as the bifurcation and folding patterns of the bronchus. The proposed Image Similarity measure is implemented in the bronchoscope navigation system that equips the prediction function of the bronchoscope motion based on Kalman filtering. The predicted results are used as initial estimations of Image registration. We applied the proposed method to eight pairs of bronchoscopic videos and three-dimensional (3-D) chest CT Images. The experimental results showed that the proposed method improved the tracking performance by five orders of magnitude over the previous method. Computation time for one frame decreased to 20% of the previous method's.

  • MICCAI (1) - New Image Similarity Measure for Bronchoscope Tracking Based on Image Registration
    Lecture Notes in Computer Science, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hirotsugu Takabatake, Junichi Hasegawa, Jun-ichiro Toriwaki, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • CARS - New calculation method of Image Similarity for endoscope tracking based on Image registration in endoscope navigation
    International Congress Series, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hiroshi Natori, Junichi Hasegawa, Jun-ichiro Toriwaki, Hirotsugu Takabatake
    Abstract:

    Abstract This paper presents a new calculation method of Image Similarity for camera motion tracking in a flexible endoscope navigation system. An endoscope navigation system is a tool which provides navigation information that is acquired from pre-operative Images to a medical doctor during an endoscopic examination in real time. In this system, one of the fundamental functions is to track endoscope camera motion. Since it is difficult to attach a positional sensor at the tip of a flexible endoscope, especially bronchoscope, due to space limitation, an Image registration technique becomes a strong tool for camera motion tracking. The previous method used Image Similarity computed by summing gray-level differences up for all pixels of real and virtual endoscopic Images. The method could not properly estimate the position and orientation of the endoscope due to averaging, since Image Similarity does not change significantly. We proposed a new Image Similarity measure which uses characteristic structure in computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are observed. Then, an Image Similarity value is calculated only within the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. In the experimental results, the proposed method showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

Zheng Liu - One of the best experts on this subject based on the ideXlab platform.

  • phase congruence measurement for Image Similarity assessment
    Pattern Recognition Letters, 2007
    Co-Authors: Zheng Liu, Robert Laganiere
    Abstract:

    In the performance assessment of an Image processing algorithm, an Image is often compared with an available reference. Measuring Image Similarity can be achieved in many ways; comparison algorithm varies from pixel-based mean square error method to structure-based Image quality index. In this paper, we present a new feature-based approach that utilizes Image phase congruency measurement to quantify the assessment of the similarities or differences between two Images. Test results with standard Images and industrial inspection Images are presented.

  • ICASSP (2) - On the Use of Phase Congruency to Evaluate Image Similarity
    2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1
    Co-Authors: Zheng Liu, Robert Laganiere
    Abstract:

    Measuring Image Similarity is important in many applications. Different algorithms propose to compare Images using pixel-based mean square error methods others use structure-based Image quality index. We present, here, a new feature-based approach that utilizes Image phase congruency measurement to quantify the assessment of the similarities or differences between two Images

Daisuke Deguchi - One of the best experts on this subject based on the ideXlab platform.

  • Selective Image Similarity measure for bronchoscope tracking based on Image registration.
    Medical image analysis, 2009
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Marco Feuerstein, Takayuki Kitasaka, Calvin R. Maurer, Yasuhito Suenaga, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • New Image Similarity measures for bronchoscope tracking based on Image registration between virtual and real bronchoscopic Images
    Medical Imaging 2004: Physiology Function and Structure from Medical Images, 2004
    Co-Authors: Kensaku Mori, Daisuke Deguchi, Takayuki Kitasaka, Yasuhito Suenaga, Hirotsugu Takabatake, Jun-ichiro Toriwaki, Tsutomu Enjoji, Hiroshi Natori
    Abstract:

    This paper describes a new method for calculating Image Similarity between a real bronchoscopic (RB) Image and a virtual endoscopic (VE) Image for bronchoscope tracking based on Image registration. Camera motion tracking is sequentially done by finding viewing parameters (camera position and orientation) that can render the most similar VE Image to a currently processing RB frame based on Image Similarity, since it is difficult to attach a positional sensor at the tip of a bronchoscope. In the previous method, Image Similarity was calculated between real and virtual endoscopic Images by summing gray-level differences up for all pixels of two Images. This method could not estimate positions and orientations of a real bronchoscope camera properly, when Image Similarity changed only a little (but partly changed significantly) due to averaging of gray-level differences for the entire Image. The proposed method divides the real and virtual endoscopic Images into a set of subregions and selects the subregions that contain characteristic shapes such as the bifurcation and folding patterns of the bronchus. The proposed Image Similarity measure is implemented in the bronchoscope navigation system that equips the prediction function of the bronchoscope motion based on Kalman filtering. The predicted results are used as initial estimations of Image registration. We applied the proposed method to eight pairs of bronchoscopic videos and three-dimensional (3-D) chest CT Images. The experimental results showed that the proposed method improved the tracking performance by five orders of magnitude over the previous method. Computation time for one frame decreased to 20% of the previous method's.

  • MICCAI (1) - New Image Similarity Measure for Bronchoscope Tracking Based on Image Registration
    Lecture Notes in Computer Science, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hirotsugu Takabatake, Junichi Hasegawa, Jun-ichiro Toriwaki, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • CARS - New calculation method of Image Similarity for endoscope tracking based on Image registration in endoscope navigation
    International Congress Series, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hiroshi Natori, Junichi Hasegawa, Jun-ichiro Toriwaki, Hirotsugu Takabatake
    Abstract:

    Abstract This paper presents a new calculation method of Image Similarity for camera motion tracking in a flexible endoscope navigation system. An endoscope navigation system is a tool which provides navigation information that is acquired from pre-operative Images to a medical doctor during an endoscopic examination in real time. In this system, one of the fundamental functions is to track endoscope camera motion. Since it is difficult to attach a positional sensor at the tip of a flexible endoscope, especially bronchoscope, due to space limitation, an Image registration technique becomes a strong tool for camera motion tracking. The previous method used Image Similarity computed by summing gray-level differences up for all pixels of real and virtual endoscopic Images. The method could not properly estimate the position and orientation of the endoscope due to averaging, since Image Similarity does not change significantly. We proposed a new Image Similarity measure which uses characteristic structure in computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are observed. Then, an Image Similarity value is calculated only within the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. In the experimental results, the proposed method showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

Hirotsugu Takabatake - One of the best experts on this subject based on the ideXlab platform.

  • Selective Image Similarity measure for bronchoscope tracking based on Image registration.
    Medical image analysis, 2009
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Marco Feuerstein, Takayuki Kitasaka, Calvin R. Maurer, Yasuhito Suenaga, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • New Image Similarity measures for bronchoscope tracking based on Image registration between virtual and real bronchoscopic Images
    Medical Imaging 2004: Physiology Function and Structure from Medical Images, 2004
    Co-Authors: Kensaku Mori, Daisuke Deguchi, Takayuki Kitasaka, Yasuhito Suenaga, Hirotsugu Takabatake, Jun-ichiro Toriwaki, Tsutomu Enjoji, Hiroshi Natori
    Abstract:

    This paper describes a new method for calculating Image Similarity between a real bronchoscopic (RB) Image and a virtual endoscopic (VE) Image for bronchoscope tracking based on Image registration. Camera motion tracking is sequentially done by finding viewing parameters (camera position and orientation) that can render the most similar VE Image to a currently processing RB frame based on Image Similarity, since it is difficult to attach a positional sensor at the tip of a bronchoscope. In the previous method, Image Similarity was calculated between real and virtual endoscopic Images by summing gray-level differences up for all pixels of two Images. This method could not estimate positions and orientations of a real bronchoscope camera properly, when Image Similarity changed only a little (but partly changed significantly) due to averaging of gray-level differences for the entire Image. The proposed method divides the real and virtual endoscopic Images into a set of subregions and selects the subregions that contain characteristic shapes such as the bifurcation and folding patterns of the bronchus. The proposed Image Similarity measure is implemented in the bronchoscope navigation system that equips the prediction function of the bronchoscope motion based on Kalman filtering. The predicted results are used as initial estimations of Image registration. We applied the proposed method to eight pairs of bronchoscopic videos and three-dimensional (3-D) chest CT Images. The experimental results showed that the proposed method improved the tracking performance by five orders of magnitude over the previous method. Computation time for one frame decreased to 20% of the previous method's.

  • MICCAI (1) - New Image Similarity Measure for Bronchoscope Tracking Based on Image Registration
    Lecture Notes in Computer Science, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hirotsugu Takabatake, Junichi Hasegawa, Jun-ichiro Toriwaki, Hiroshi Natori
    Abstract:

    This paper presents new Image Similarity measure for bronchoscope tracking based on Image registration between real and virtual endoscopic Images. A function for bronchoscope tracking is one of the fundamental functions in a bronchoscope navigation system. Since it is difficult to attach a positional sensor at the tip of the bronchoscope due to the space limitation, Image registration between real endoscopic (RE) and virtual endoscopic (VE) Images becomes a strong tool for bronchoscopic camera motion tracking. The summing-type Image Similarity measuring methods including mean squared error or mutual information could not properly estimate the position and orientation of the endoscope, since the outputs of these methods do not change significantly due to averaging. This paper proposes new Image Similarity measure that effectively uses characteristic structures observed in bronchoscopic views in Similarity computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are seen. Then, an Image Similarity value is calculated only inside the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. The experimental showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.

  • CARS - New calculation method of Image Similarity for endoscope tracking based on Image registration in endoscope navigation
    International Congress Series, 2003
    Co-Authors: Daisuke Deguchi, Kensaku Mori, Yasuhito Suenaga, Hiroshi Natori, Junichi Hasegawa, Jun-ichiro Toriwaki, Hirotsugu Takabatake
    Abstract:

    Abstract This paper presents a new calculation method of Image Similarity for camera motion tracking in a flexible endoscope navigation system. An endoscope navigation system is a tool which provides navigation information that is acquired from pre-operative Images to a medical doctor during an endoscopic examination in real time. In this system, one of the fundamental functions is to track endoscope camera motion. Since it is difficult to attach a positional sensor at the tip of a flexible endoscope, especially bronchoscope, due to space limitation, an Image registration technique becomes a strong tool for camera motion tracking. The previous method used Image Similarity computed by summing gray-level differences up for all pixels of real and virtual endoscopic Images. The method could not properly estimate the position and orientation of the endoscope due to averaging, since Image Similarity does not change significantly. We proposed a new Image Similarity measure which uses characteristic structure in computation. This method divides the original Image into a set of small subblocks and selects only the subblocks in which characteristic shapes are observed. Then, an Image Similarity value is calculated only within the selected subblocks. We applied the proposed method to eight pairs of X-ray CT Images and real bronchoscopic videos. In the experimental results, the proposed method showed much improvement in continuous tracking performance. Nearly 1000 consecutive frames were tracked correctly.