A medical image segmentation method

Abstract

The invention relates to a medical image segmentation method, which belongs to the field of image processing. The method of the present invention is based on a harmony search algorithm, and is realized through steps such as image preprocessing, image feature extraction, image pre-segmentation, global optimal harmony search, and optimal harmony search in partial areas of the image. Theory and practice prove that the present invention properly solves the problems of single objective function, single threshold and no consideration of component clustering effect in the clustering algorithm. After adopting the method of the present invention, not only the segmentation of medical images can be efficiently completed, but also important image features will not be lost, so high-quality medical segmentation images can be obtained to meet the diagnostic reading needs of medical staff.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to a medical image segmentation method, which belongs to the field of image processing. Background technique [0002] With the rapid development of human medical imaging technologies such as Computed Tomography (CT) and MRI (Megnectic Resonance lmaging, MRI), medical images are playing an increasingly important role in clinical medical diagnosis. [0003] As an unsupervised learning algorithm, clustering is used in many practical problems due to its simplicity, ease of operation, and good robustness, such as data mining, image processing, bioinformatics, decision-making and planning, and so on. Clustering method is also one of the methods often used in image segmentation. The main idea of ​​this type of method is to divide the features into multiple categories according to the features in the image data and according to a certain clustering algorithm idea. The image features in the s...

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Problems solved by technology

[0008] The purpose of the present invention is to propose a medical image segmentation method that fully considers the image as a whole and each component, and enables them to achieve optimal results in view of the limitations of the above-mentioned prior art, thereby effectively solving most of the clustering problems. The algorithm only has a single objective optimization function and does not consider the defects of the clustering effect of the components, which significantly improves the anti-noise ability, makes the clustered image closer to the objective and real situation of each object, and provides more accurate medical diagnosis. image based on

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