Multi-elite immune quantum clustering-based medical image segmenting system and multi-elite immune quantum clustering-based medical image segmenting method

Abstract

The invention discloses a multi-elite immune quantum clustering-based medical image segmenting system and a multi-elite immune quantum clustering-based medical image segmenting method, which relate to the technical field of image processing. The system comprises a preprocessing module, a data preparing module, a data clustering module and a segmentation result output module. The process for segmenting a medical image by the modules comprises the following steps: 1) preprocessing the medical image to be segmented; 2) coding antibodies and initializing an antibody population; 3) calculating antibody affinity, and dividing the antibody population into an elite population and a general population; 4) designing different multi-elite immune optimization operators for the elite population and the general population respectively, and performing a cloning operation, a cloud mutation operation, an all-interference recombination operation, a selecting operation and a hypercube interlace operation orderly; and 5) outputting a medical image segmentation result. The multi-elite immune quantum clustering-based medical image segmenting system and the multi-elite immune quantum clustering-based medical image segmenting method can effectively segment the medical image which contains large-scale data volume, has an accurate and precise segmentation result, and can be used for auxiliary diagnosisof the medical image and pathogenesis research.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to medical image segmentation, and can be used for medical image auxiliary diagnosis and research on pathogenesis. Background technique [0002] With the rapid development of computer technology, computer-aided diagnosis is playing an increasingly important role in clinical medicine. The traditional method of judging the pathogenesis with the naked eye has been gradually replaced by X-ray, CT and MRI technologies, which greatly promote the development of medical imaging and disease research. [0003] Image segmentation is to divide the image into several meaningful parts according to a certain principle of uniformity or consistency, and extract the object of interest from a very complex background for further analysis. Medical imaging is affected by factors such as small gray scale differences, uneven image blur, noise, and many pathological categories, making the auxiliary diag...

AI Technical Summary

Problems solved by technology

However, quantum clustering is easy to fall into local extremum when iterating through the gradient descent method. At the same time, the slow iteration speed limits its application in large-scale data sets, especially in the field of image segmentation.

Although there are some improved technologies, such as improvements to distance measures, improvements to scale parameter estimation, etc., none of them can fundamentally solve the above bottleneck problems

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