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Medical image segmentation method based on combination of cloud module and image segmentation

A medical image, combined technology, applied in the field of image processing, can solve problems such as poor versatility, achieve good segmentation effect, improve accuracy, and good numerical robustness.

Active Publication Date: 2016-03-09
宁波金唐软件有限公司
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Problems solved by technology

[0004] Although these mixed methods have better image segmentation results under certain conditions, their generality is poor.

Method used

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  • Medical image segmentation method based on combination of cloud module and image segmentation
  • Medical image segmentation method based on combination of cloud module and image segmentation
  • Medical image segmentation method based on combination of cloud module and image segmentation

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Embodiment Construction

[0042] The present invention will be further explained below in conjunction with the drawings:

[0043] Such as figure 1 As shown, the method flow of the present invention is as figure 1 Shown.

[0044] The invention relates to a cloud model, which mainly includes a reverse cloud generator, a cloud comprehensive algorithm and an X-condition cloud generator.

[0045] figure 2 The reverse cloud generator shown is a model that realizes the uncertainty conversion between a numerical value and its linguistic value, and is a mapping from quantitative to qualitative. It effectively converts a certain amount of precise data into concepts represented by qualitative language expectations Ex, entropy En, and hyper-entropy He, and uses this to represent the entire cloud drop reflected by these precise data.

[0046] Cloud integration refers to the integration of the two or several bottom clouds that are closest to each other to generate a new high-level cloud.

[0047] image 3 The X-condition ...

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Abstract

The invention request to protect a medical image segmentation method based on the combination of a cloud module and image segmentation. The method comprises the steps of: firstly, carrying out smoothing processing on an image, and removing noise points; then utilizing reverse cloud conversion to cloud characteristic constants of an image foreground and background respectively, and utilizing an X condition cloud generator to calculate membership degrees of each pixel relative to the foreground and background; calculating data items and smooth items; then establishing an energy function to construct a corresponding network figure, and utilizing a maximum flow / minimum cut algorithm to realize medical image segmentation; and finally, judging whether a segmentation result meets iteration conditions, if yes, then outputting the result, and otherwise, calculating cloud characteristic constants of a current segmentation result foreground and background again. According to the invention, the cloud module and the image segmentation algorithm are combined, the good multi-characteristic constraint capability and the global optimality of the image segmentation method are reserved, and the fuzziness and randomness of the cloud model and nondeterminacy of the association between the cloud module and the image segmentation algorithm are introduced, so that the precision of medical image segmentation can be effectively improved.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and particularly relates to a medical image segmentation method based on a combination of cloud model and graph cutting. Background technique [0002] In recent years, with the rapid development and popularization of new imaging technologies and equipment such as computed tomography (CT), magnetic resonance imaging (magnetic resonance imaging, MRI), and positron emission tomography (PET), medical institutions around the world Massive amounts of medical impact data are generated every day, which makes medical imaging one of the fastest growing fields in medical technology. Among them, the segmentation technology based on computer-aided diagnosis (CAD) is a powerful auxiliary method for radiologists to diagnose. Segmentation is not only a particularly important processing step for inspection and analysis, but also a bottleneck restricting the development of visualization, registration, fusion,...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/20116
Inventor 李伟生王莹赖均王国胤
Owner 宁波金唐软件有限公司
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