Improved QPSO based FCM medical image segmentation method

Abstract

The invention introduces the fuzzy clustering technology according to the characteristics that boundaries between different soft tissues or the soft tissues and lesions are fuzzy and fine structure distribution is complex, performs optimization by using the improved quantum particle swarm optimization algorithm, and provides an image segmentation method on the above basis. The invention uses a newimproved quantum particle swarm optimization algorithm to effectively overcome the defect that the standard fuzzy C-means fuzzy clustering algorithm relies on an initial cluster center, which is prone to local optimum, so that the medical image is segmented well. Since the method can always effectively converge after initial conditions are given, the method has a good effect for dealing with problems such as blurring and unclear boundaries that are often present in medical images. The method can preserve more original information in the process of processing medical images, and the robustnessof the method is higher than that of other segmentation algorithms such as hard clustering.

Description

technical field [0001] The invention discloses a QPSO-based FCM medical image segmentation method, belongs to the technical field of image processing, and is mainly used for medical image processing. Background technique [0002] Medical imaging equipment provides a wealth of image information for medical diagnosis, which contains a lot of useful disease diagnosis and analysis information. Effective use of these medical image information can effectively help doctors to carry out computer-aided diagnosis, implement interventional treatment, formulate medical and surgical operation planning, dynamically simulate corresponding medical tissues and organs, and analyze the structure and occurrence process of lesion parts, so as to improve the accuracy of disease diagnosis. Accuracy. [0003] Typically, medical images are very complex. This is due to the complexity of the anatomical structure of the human body and the inevitable effects of noise, field offset effects, local volum...

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Problems solved by technology

However, this type of method also has the following defects: it is difficult to judge how many types of medical images should be divided into unknown and complex structures, and the FCM clustering algorithm has a strong dependence on the initial cluster center and the membership matrix, which is easy to fall into a local optimum untie

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