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Local three-dimensional maximum inter-class variance segmentation method of three-dimensional CT image

A technology of maximum inter-class variance, CT image, applied in the field of image processing, can solve the problems of mis-segmentation, difficult segmentation, and uneven grayscale of CT images, achieve high computational efficiency, improve noise resistance, and increase the amount of statistical information Effect

Active Publication Date: 2018-02-27
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to solve the difficult segmentation and mis-segmentation problems caused by the uneven gray level of CT images, the present invention provides a local three-dimensional maximum inter-class variance segmentation method for three-dimensional CT images

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  • Local three-dimensional maximum inter-class variance segmentation method of three-dimensional CT image
  • Local three-dimensional maximum inter-class variance segmentation method of three-dimensional CT image
  • Local three-dimensional maximum inter-class variance segmentation method of three-dimensional CT image

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Embodiment Construction

[0051] This method is used to segment the cone-beam CT sequence slice image img_s with a resolution of 1024×1024, a gray level of 256, and a number of 301 slices. In this use case, the size of the local statistical window is 5×5×5, the number of image layers in the three-dimensional local space is 9, and the width of the edge transition area is 17. According to these information set parameters K=301, L=256, perform the following steps:

[0052] Step 1. Initialization:

[0053] (1) Set the size of the statistics calculation window as 5×5×5, that is, the side length of the window is l=5, and the half length of the window is v=(l−1) / 2=2.

[0054] (2) Set the size of the weight template w to be 5×5×5, and its calculation method is as in formula (1), where (c x ,c y ,c z ) is the coordinate relative to the center of the window, The function is to round up. The calculation results are as follows, which correspond to the layers from bottom to top in the weight template from le...

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Abstract

The present invention discloses a local three-dimensional maximum inter-class variance segmentation method of a three-dimensional CT image, and belongs to the field of image segmentation, in order tosolve difficult segmentation and mis-segmentation problems caused by a non-uniform gray phenomenon of a CT image. The method mainly comprises the following steps: (1) initializing the size of the statistics calculation window, the weight template and the size of the three-dimensional local space, and carrying out continuation processing on the original image; (2) using the one-dimensional maximuminter-class variance segmentation algorithm and the edge tracking algorithm to extract the target contour of each layer of the image, and then using the morphological dilation method to obtain the marker image of the edge transition region; and (3) according to the pixel gray level, the weighted average of the neighborhood, and the weighted median of the neighborhood, calculating the three-dimensional histogram of each three-dimensional local space, and constructing a lookup table by using a recursive method to obtain the optimal segmentation threshold. Compared with the prior art, the methodprovided by the present invention has advantages that a three-dimensional CT image with uneven grayscale can be segmented and the time complexity is O(L3).

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a local three-dimensional maximum inter-class variance segmentation method of three-dimensional CT images. Background technique [0002] CT is an imaging technology with strong comprehensive detection ability, which has been widely used in medical and industrial fields. A set of continuous, equally spaced CT images is usually called a 3D CT image. At present, 2D CT based on linear array detectors, cone beam CT based on area array detectors, and spiral CT based on multi-row detectors can all obtain 3D CT images through corresponding scanning reconstruction methods. [0003] Segmentation is an essential and important link when performing detection and analysis based on CT images. Due to the unfavorable factors such as poor gray uniformity, difficult to completely eliminate artifacts and high noise level in CT images, the accuracy achieved by traditional segmentation algorithms is lo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/136G06T7/174G06T7/194
CPCG06T2207/10081G06T2207/20021G06T7/11G06T7/136G06T7/174G06T7/194
Inventor 黄魁东廖金明张定华
Owner NORTHWESTERN POLYTECHNICAL UNIV
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