Clustering integration method for image data of X-ray films

Abstract

The invention discloses a clustering integration method for the image data of X-ray films. The method comprises the steps of S01, pretreating the image of an X-ray film to obtain the data of the image; S02, obtaining a gray value (Gi, j) at each point of the image and storing obtained gray values in a gray value matrix G, wherein the gray value (Gi, j) represents the gray value of a point at the i row and the j column of the matrix; S03, conducting the clustering analysis and treatment on the gray value matrix G according to the k-means improved algorithm-based clustering integration algorithm or the hierarchical clustering-based improved algorithm; S04, conducting the integration operation according to the HGPA algorithm. According to the k-means improved algorithm-based clustering integration algorithm, the selection of k initial cluster centers is optimized. According to the hierarchical clustering-based improved algorithm, data are simplified during the data pretreating process. In this way, points of the same gray value in the matrix are divided in the same cluster, and the number of initial clusters is 256 at most. Therefore, the observation difficulty of X-ray films is lowered, and even exogenous foreign matters can be found out. As a result, the method facilitates the doctor diagnosing process.

Description

Technical field [0001] The invention relates to a clustering integration method, in particular to a clustering integration method for X-ray film image data. Background technique [0002] With the application of various technologies in the medical field, traditional diagnostic radiology has become a basic part of medical imaging. X-ray photography is the most commonly used medical examination method in clinic. It is suitable for almost any part of the human body. It has the characteristics of high spatial resolution and clear tomographic images, and is often used as an auxiliary tool for medical diagnosis. Although X-ray film has the above advantages, the three-dimensional human body is displayed as a two-dimensional image during X-ray film imaging, so there will be overlap and distortion in the display of human organs, and its density resolution is not as good as CT , Ultrasound and MRI. [0003] Because of the various problems mentioned above, the clustering technology is applie...

AI Technical Summary

Problems solved by technology

However, for data X-ray images, the high time complexity and space complexity of the hierarchical clustering algorithm will cause each experiment to consume a lot of time and memory space, and even cause memory overflow errors

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