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Rat brain section microscopic image segmentation method based on markov random field theory

A Markov random field and microscopic image technology, applied in image analysis, image data processing, instruments, etc., can solve the problem that Gaussian distribution cannot accurately describe noise and cell grayscale information.

Inactive Publication Date: 2013-07-17
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes a mouse brain slice microscopic image segmentation method based on the Markov random field theory to solve the problem that the independent Gaussian distribution cannot accurately describe the gray level information of noise and cells

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  • Rat brain section microscopic image segmentation method based on markov random field theory
  • Rat brain section microscopic image segmentation method based on markov random field theory
  • Rat brain section microscopic image segmentation method based on markov random field theory

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Embodiment Construction

[0054] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0055] The hardware environment used for implementation is: Intel Core 2 Duo 2.93G computer, 2.0GB memory, 512M graphics card, and the running software environment is: Windows XP. We have realized the method that the present invention proposes with Matlab7.0 software. The image database includes 600 microscopic images of mouse brain slices with a resolution of 732×732, 400 of which have been segmented and used to train the parameters of the Gaussian mixture model; the remaining 200 are images to be segmented.

[0056] The present invention is specifically implemented as follows:

[0057] Step 1: Learn the parameters of the Gaussian mixture distribution: use the binary Gaussian mixture distribution to model the microscopic image of the mouse brain slice, and estimate the parameters of the Gaussian mixture distribution through the expectation maximization algorith...

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Abstract

The invention relates to a rat brain section microscopic image segmentation method based on a markov random field theory. Gaussian mixed distribution is trained through the existing tagged image and used in characteristic field modeling, image characteristics are simulated correctly, great guiding significance for random field modeling is obtained, convergence times of an iterative algorithm are greatly reduced, and the accuracy of a segmentation result is improved. In addition, in order to solve the problem that local neighborhood characteristics of a traditional 8 neighborhood pixel model traced image are too rough, the method introduces a pixel gray value and a distance between pixels into a Potts model, a new potential-energy function is defined, image local information is described correctly, and the accuracy of a segmentation result is improved.

Description

technical field [0001] The invention belongs to the technical field of biological microscopic image processing, and in particular relates to a method for segmenting microscopic images of mouse brain slices based on Markov random field theory. Background technique [0002] Segmentation of cells in microscopic images is a fundamental problem in the research fields of biology and life sciences. Microscopic images of mouse brain slices are images of brain tissue observed under a high-resolution microscope, in which accurate identification of nerve cells is of great significance for in-depth analysis of biological genetic and metabolic mechanisms. In the early stage of the experiment, the green fluorescent protein gene was added to the FOS gene of the mouse. When the FOS gene was expressed into C-FOS protein in the nerve cells, the fluorescent protein gene bound to it was expressed at the same time. Through molecular microscope photography, the fluorescent protein gene containing...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 韩军伟孙立晔郭雷胡新韬
Owner NORTHWESTERN POLYTECHNICAL UNIV
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