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Mingliang Zhou - One of the best experts on this subject based on the ideXlab platform.

  • active Contour driven by adaptively weighted signed pressure force combined with legendre polynomial for image segmentation
    Information Sciences, 2021
    Co-Authors: Bin Fang, Mingliang Zhou, Sam Kwong
    Abstract:

    Abstract This paper proposes an active Contour driven by adaptively weighted signed pressure force (SPF) combined with the Legendre polynomial method for image segmentation. First, an adaptively weighted global average intensity (GAI) term is defined wherein GAI differences are the weighted factors of the interior and exterior region-driving centers. Second, an adaptively weighted Legendre polynomial intensity (LPI) term is defined which adopts the Legendre polynomial intensity average differences as the weighted factors of the interior and exterior region-driving centers. Finally, the GAI and LPI terms are introduced into a novel SPF function and a coefficient is applied to weight their effect degrees; a new edge stopping function (ESF) is defined and combined with the region-based method to robustly converge the curve to the boundary of the object. Experiments demonstrate that this method is highly accurate and computationally efficient for images with inhomogeneous intensity, blurred edge, low contrast, and noise problems. Moreover, the segmentation results are independent of the Initial Contour.

  • hybrid active Contour driven by double weighted signed pressure force for image segmentation
    International Conference on Acoustics Speech and Signal Processing, 2020
    Co-Authors: Bin Fang, Mingliang Zhou
    Abstract:

    In this paper, we proposed a novel hybrid active Contour driven by double-weighted signed pressure force method for image segmentation. First, the Legendre polynomials and global information are integrated into the signed pressure force (SPF) function and a coefficient is applied to weight the effect degrees of the Legendre term and global term. Second, by introducing a weighted factor as the coefficient of inside and outside region fitting center, the curve can be optimally evolved to the interior and branches of the region of interest (ROI). Third, a new edge stopping function is adopted to robustly capture the edge of ROI and speed up the multi-object image segmentation. Experiments show that the proposed method can achieve better accuracy for images with noise, inhomogeneous intensity, blur edge and complex branches, in the meanwhile, it also controls the time-consuming effectively and is insensitive to the Initial Contour position.

Chung-ming Chen - One of the best experts on this subject based on the ideXlab platform.

  • cell based dual snake model a new approach to extracting highly winding boundaries in the ultrasound images
    Ultrasound in Medicine and Biology, 2002
    Co-Authors: Chung-ming Chen, Yueng Shiang Huang
    Abstract:

    Two common deficiencies of most conventional deformable models are the need to place the Initial Contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant Initial Contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 ± 0.42 pixels and 1.29 ± 0.39 pixels for the synthetic and the clinical US images, respectively.

  • A dual-snake model of high penetrability for ultrasound image boundary extraction.
    Ultrasound in medicine & biology, 2001
    Co-Authors: Chung-ming Chen, An-ting Hsiao
    Abstract:

    Abstract Most deformable models require the Initial Contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant Initial Contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant Initial Contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR ≥ 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw)

  • An early vision-based snake model for ultrasound image segmentation.
    Ultrasound in medicine & biology, 2000
    Co-Authors: Chung-ming Chen, Yu-chen Lin
    Abstract:

    Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model. By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the Initial Contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model.

Bin Fang - One of the best experts on this subject based on the ideXlab platform.

  • active Contour driven by adaptively weighted signed pressure force combined with legendre polynomial for image segmentation
    Information Sciences, 2021
    Co-Authors: Bin Fang, Mingliang Zhou, Sam Kwong
    Abstract:

    Abstract This paper proposes an active Contour driven by adaptively weighted signed pressure force (SPF) combined with the Legendre polynomial method for image segmentation. First, an adaptively weighted global average intensity (GAI) term is defined wherein GAI differences are the weighted factors of the interior and exterior region-driving centers. Second, an adaptively weighted Legendre polynomial intensity (LPI) term is defined which adopts the Legendre polynomial intensity average differences as the weighted factors of the interior and exterior region-driving centers. Finally, the GAI and LPI terms are introduced into a novel SPF function and a coefficient is applied to weight their effect degrees; a new edge stopping function (ESF) is defined and combined with the region-based method to robustly converge the curve to the boundary of the object. Experiments demonstrate that this method is highly accurate and computationally efficient for images with inhomogeneous intensity, blurred edge, low contrast, and noise problems. Moreover, the segmentation results are independent of the Initial Contour.

  • hybrid active Contour driven by double weighted signed pressure force for image segmentation
    International Conference on Acoustics Speech and Signal Processing, 2020
    Co-Authors: Bin Fang, Mingliang Zhou
    Abstract:

    In this paper, we proposed a novel hybrid active Contour driven by double-weighted signed pressure force method for image segmentation. First, the Legendre polynomials and global information are integrated into the signed pressure force (SPF) function and a coefficient is applied to weight the effect degrees of the Legendre term and global term. Second, by introducing a weighted factor as the coefficient of inside and outside region fitting center, the curve can be optimally evolved to the interior and branches of the region of interest (ROI). Third, a new edge stopping function is adopted to robustly capture the edge of ROI and speed up the multi-object image segmentation. Experiments show that the proposed method can achieve better accuracy for images with noise, inhomogeneous intensity, blur edge and complex branches, in the meanwhile, it also controls the time-consuming effectively and is insensitive to the Initial Contour position.

Sam Kwong - One of the best experts on this subject based on the ideXlab platform.

  • active Contour driven by adaptively weighted signed pressure force combined with legendre polynomial for image segmentation
    Information Sciences, 2021
    Co-Authors: Bin Fang, Mingliang Zhou, Sam Kwong
    Abstract:

    Abstract This paper proposes an active Contour driven by adaptively weighted signed pressure force (SPF) combined with the Legendre polynomial method for image segmentation. First, an adaptively weighted global average intensity (GAI) term is defined wherein GAI differences are the weighted factors of the interior and exterior region-driving centers. Second, an adaptively weighted Legendre polynomial intensity (LPI) term is defined which adopts the Legendre polynomial intensity average differences as the weighted factors of the interior and exterior region-driving centers. Finally, the GAI and LPI terms are introduced into a novel SPF function and a coefficient is applied to weight their effect degrees; a new edge stopping function (ESF) is defined and combined with the region-based method to robustly converge the curve to the boundary of the object. Experiments demonstrate that this method is highly accurate and computationally efficient for images with inhomogeneous intensity, blurred edge, low contrast, and noise problems. Moreover, the segmentation results are independent of the Initial Contour.

An-ting Hsiao - One of the best experts on this subject based on the ideXlab platform.

  • A dual-snake model of high penetrability for ultrasound image boundary extraction.
    Ultrasound in medicine & biology, 2001
    Co-Authors: Chung-ming Chen, An-ting Hsiao
    Abstract:

    Abstract Most deformable models require the Initial Contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant Initial Contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant Initial Contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR ≥ 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw)