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Image segmentation method and system based on fuzzy C-means and probability label fusion

A label fusion and image segmentation technology, applied in the field of medical image processing, can solve the problems of discontinuity, inability to achieve lesion image segmentation, complex gray distribution, etc., and achieve the effect of accurate segmentation.

Active Publication Date: 2020-11-13
SHANDONG NORMAL UNIV
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

Although the threshold method is simple and easy to use, because the gray distribution of each tissue in the brain MRI image is more complex, it is difficult to determine the threshold between each tissue, so it is generally not used alone
The main disadvantage of the region growing method is that it requires manual selection of seed points. In addition, although the algorithm is less sensitive to noise than the threshold method, it may form holes or even discontinuous regions in the extracted shape.
[0006] The lesion segmentation method for MRI images in the prior art has low segmentation accuracy and cannot achieve lesion image segmentation under unsupervised conditions

Method used

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  • Image segmentation method and system based on fuzzy C-means and probability label fusion
  • Image segmentation method and system based on fuzzy C-means and probability label fusion
  • Image segmentation method and system based on fuzzy C-means and probability label fusion

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Embodiment 1

[0029] This embodiment provides an image segmentation method based on fusion of fuzzy C-means and probability labels;

[0030] Such as figure 1 As shown, the image segmentation method based on fuzzy C-means and probability label fusion, including:

[0031] S101: Using the fuzzy C-means algorithm with superpixels as the clustering center, perform lesion segmentation on the acquired brain MRI images, and obtain initial lesion segmentation results of images of different modalities;

[0032] S102: Based on the multimodal probability label fusion algorithm, perform probability label fusion on initial lesion segmentation results of different modal images to obtain a final lesion segmentation result.

[0033] As one or more embodiments, in the S101, the brain MRI image is segmented using the fuzzy C-means algorithm with superpixels as the clustering center to obtain the initial lesion segmentation results of images of different modalities; the specific steps include:

[0034] S101-...

Embodiment 2

[0149] This embodiment provides an image segmentation system based on fusion of fuzzy C-means and probability labels;

[0150] An image segmentation system based on fuzzy C-means and probabilistic label fusion, including:

[0151] The initial segmentation module is configured to: use the fuzzy C-means algorithm with superpixels as the clustering center to perform lesion segmentation on the acquired brain MRI images, and obtain initial lesion segmentation results of images of different modalities;

[0152] The probability label fusion module is configured to perform probability label fusion on initial lesion segmentation results of different modal images based on a multimodal probability label fusion algorithm to obtain a final lesion segmentation result.

[0153]It should be noted here that the above-mentioned initial segmentation module and probability label fusion module correspond to steps S101 to S102 in the first embodiment, and the examples and application scenarios impl...

Embodiment 3

[0157] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0158] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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Abstract

The invention discloses an image segmentation method and system based on fuzzy C-means and probability label fusion, and the method comprises the steps: carrying out the lesion segmentation of an obtained brain nuclear magnetic resonance image through employing a fuzzy C-means algorithm employing a superpixel as a clustering center, and obtaining initial lesion segmentation results of different modal images; and based on a multi-modal probability label fusion algorithm, performing probability label fusion on the initial lesion segmentation results of the different modal images to obtain a final lesion segmentation result. The segmentation of each mode of the nuclear magnetic resonance image is carried out by using an improved fuzzy C-means algorithm based on superpixels. And the segmentation advantages of different modes are fused to generate an optimal segmentation result. The segmentation of the three modes of the nuclear magnetic image has advantages, and the method fuses the segmentation results of different modes to obtain a more accurate segmentation effect.

Description

technical field [0001] The present disclosure relates to the technical field of medical image processing, in particular to an image segmentation method and system based on fusion of fuzzy C-means and probability labels. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] Multiple sclerosis (Multiple Sclerosis lesion, MS lesion) is a common, chronic degenerative disease in the human central nervous system. The main symptoms include numbness and weakness of limbs, incoordination, dizziness or impairment of visual function. According to the latest epidemiological studies, the incidence of multiple sclerosis has been increasing worldwide. Because magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is highly sensitive for detecting multiple sclerosis lesions and can quantitatively evaluate lesion volume, MRI images have become the most importan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/143G06K9/62
CPCG06T7/143G06T2207/10088G06F18/23213G06F18/25
Inventor 王晶晶刘美如高军任金雯
Owner SHANDONG NORMAL UNIV
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