Brain magnetic resonance image super-voxel generating method based on prior knowledge

A technology of magnetic resonance images and prior knowledge, applied in the field of supervoxel generation of brain magnetic resonance images based on prior knowledge, can solve the problems of unavailability, high MRI noise, weak boundaries, etc., achieving high efficiency and suppressing noise. , the effect of high boundary fit

Active Publication Date: 2017-09-08
SOUTHEAST UNIV
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Problems solved by technology

[0004] However, for different types of images, current superpixel generation algorithms generally have mutual constraints between the number of superpixels, compactness, segmentation quality, and algorithm practicability.
At the same time, it is difficult to obtain better

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  • Brain magnetic resonance image super-voxel generating method based on prior knowledge
  • Brain magnetic resonance image super-voxel generating method based on prior knowledge
  • Brain magnetic resonance image super-voxel generating method based on prior knowledge

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[0052] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0053] The present invention provides a supervoxel generation method for brain magnetic resonance images based on prior knowledge. The probability distribution maps of three tissues are registered to individual templates. After selecting seed points, a new method is designed by integrating prior knowledge. Measure operator, which clusters voxels to seed points to generate supervoxels. The specific process of the present invention is as figure 1 shown, including the following steps:

[0054] Step 1, the probability map is registered to the individual space.

[0055] Registration is used to obtain the spatial transformation of the template image transformed into ...

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Abstract

The invention discloses a brain magnetic resonance image super-voxel generating method based on the prior knowledge. The method comprises the following steps: taking the weight of the space distance, the pixel intensity and the prior knowledge as the final distance measurement based on a K-means clustering algorithm, clustering the image pixel, and segmenting the brain MRI image into a series of uniform super-voxels capable of preferably fitting with the image edge. By integrating with the prior knowledges of different tissues of the brain, a new measure operator is designed, a robust super-voxel generating method is constructed to realize the super-voxel segmentation of the brain magnetic resonance image, and the influence on the segmentation result by the image noise can be reduced. Compared with the known super-voxel generating method, the method disclosed by the invention has high efficiency, higher boundary fitting degree, and is capable of preferably inhibiting the influence of the noise.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, and relates to a processing method of brain magnetic resonance images, and more specifically, relates to a supervoxel generation method of brain magnetic resonance images based on prior knowledge. Background technique [0002] Superpixel (superpixel) or supervoxel (supervoxel) is an image preprocessing technology that has developed rapidly in recent years. Compared with the basic unit in traditional processing methods—pixels, superpixels are more conducive to the extraction of local features and the expression of structural information, and can greatly reduce the computational complexity of subsequent processing. It has been obtained in the field of computer vision, especially in image segmentation. widely used. In addition, superpixels have also been introduced into other computer vision research fields such as object tracking, human pose estimation, object recognition,...

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Application Information

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IPC IPC(8): G06T7/11G06K9/62G06T7/33
CPCG06T2207/20156G06T2207/30016G06T2207/10088G06F18/23213G06F18/22
Inventor 孔佑勇任洲甫左雨林沈傲东伍家松舒华忠
Owner SOUTHEAST UNIV
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