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Method for drawing dynamic Gaussian volume model for three-dimensional medical images

A medical image, three-dimensional image technology, applied in the field of image processing, can solve the problems of interaction complexity, loss of structural information, blurred target area, etc.

Active Publication Date: 2013-04-17
SOUTHERN MEDICAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0006] The purpose of the present invention is to provide a method for drawing a dynamic Gaussian volume model of a three-dimensional medical image, which can clearly display the region of interest while retaining the relevant structural information around the region of interest, so as to overcome the interaction caused by methods such as sectioning Undesirable phenomena such as loss of complexity and structural information and blurring of target regions in existing methods

Method used

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  • Method for drawing dynamic Gaussian volume model for three-dimensional medical images
  • Method for drawing dynamic Gaussian volume model for three-dimensional medical images
  • Method for drawing dynamic Gaussian volume model for three-dimensional medical images

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Embodiment Construction

[0038] The following is the specific process of imaging the CT data of a set of heads by using the three-dimensional medical image dynamic Gaussian volume model drawing method of the present invention:

[0039] The data source used in the experiment is the OsiriX website; the GPU is a Quadro FX 1700 graphics card.

[0040] Step 1, read in the CT image, the size of the image is 512×512×460, first normalize the image sequence data, and calculate the maximum texture coordinates to satisfy the condition: T c =2 n , c ∈ {x, y, z}. T c is the texture coordinate in the c direction, n is to satisfy 2 n ≥S c The smallest integer of S c is the dimensionality of the image sequence in the c direction. Save the image sequence as a 3D texture for density value indexing of 3D volume data.

[0041] Step 2, the glGetFloatv function of the Open Graphics Library (OpenGL, Open Graphics Library) can obtain a 4X4 model-view matrix, and then calculate the inverse matrix invMVM[4X4] of the mat...

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Abstract

The invention discloses a method for drawing dynamic Gaussian volume model for three-dimensional medical images. The method comprises the following steps of: (a) saving an image sequence as a three-dimensional texture for later texture retrieval sampling; (b) respectively leading out a ray from viewpoints to all pixels on the outermost layer of the surface of the image sequence so as to pass through volume data, and performing uniformly-spaced sampling on every ray to obtain respective density values of sampling points; (c) mapping the respective density values of the sampling points into colors and opaqueness by using transfer functions; (d) establishing a Gaussian model according to distance and angle information of current sampling points and a focus, and correcting the respective opaqueness of the sampling points; and (e) accumulating the respective opaqueness and colors of all the sampling points along the light direction to finally obtain a color value of one point projected to a screen. According to the method, an internal structure in a focus area can be clearly displayed, and meanwhile, organization structures around the focus are reserved. Different from traditional unified sectioning method, the method disclosed by the invention achieves an effect of the focus with the context.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for drawing a dynamic Gaussian volume model of a three-dimensional medical image. Background technique [0002] Volume rendering technology is an important aspect of medical visualization research, especially in medical diagnosis and auxiliary surgery, and has become an indispensable tool. With the rapid development of medical imaging equipment, the amount of data generated by the equipment is gradually increasing, and the tissues or parts of clinical interest are relatively small compared to the whole body data, such as blood vessels in the head, kidney or liver Internal lesions, lung tumors, etc. It is a very complicated task for doctors to draw conclusions through observation of data, and the anatomical structure of suspicious lesion tissue must be taken into account, including the size and shape of the lesion, as well as the relationship between its spatial...

Claims

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Application Information

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IPC IPC(8): G06T17/00G06T15/08
Inventor 冯前进陈武凡王文化贠照强
Owner SOUTHERN MEDICAL UNIVERSITY
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