A medical image clustering method

Abstract

The invention relates to a medical image clustering method, and belongs to the field of image processing. The method is based on a downhill algorithm, and comprises the following steps of preprocessing an image, extracting an image characteristic, searching a density attractor, searching hill foot pixels, and separating a layout image. Theories and practices prove that the method achieves a ridge, a flat top and a single threshold appropriately, and after the method is adopted, the medical image can be clustered efficiently, and the image characteristic is not lost, misled or omitted, so that the high-quality medical clustering image can be obtained, and diagnosis and reading requirements of medical workers are met.

Description

technical field

[0001] The invention relates to an image clustering method, in particular to a medical image clustering 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 Imaging, MRI), medical images are playing an increasingly important role in clinical medical diagnosis.

[0003] Cluster analysis is one of the main techniques of data mining, and it is an unsupervised learning process. According to some attributes of things, it gathers things into classes, so that the similarity within a class is as small as possible, and the similarity between classes is as large as possible. Cluster analysis has been widely used in many fields, including pattern recognition, data analysis, image processing, and market research. Using cluster analysis to classify target objects in a single image is called image clustering. Ima...

Examples

Embodiment Construction

[0038] The medical image clustering method will be further described below mainly in conjunction with the accompanying drawings and specific embodiments.

[0039] In this embodiment, the brain MRI map is selected for analysis, and the original brain MRI containing noise points is selected. figure 2 a to illustrate the corresponding results after the implementation of the present invention, the concrete steps are as follows:

[0040] A. The computer reads the original image of the brain MRI map, and obtains the interval value of the image pixel point through the relationship between the image pixel point and its surrounding 8 neighborhood information, and uses the interval value each of the image like

[0041] B. According to the rough set principle, the left interval of each pixel interval value x N and the right interval Approximate into upper approximation and lower approximation, calculate the roughness ρ of each interval value A (x k ).

[0042] C. Draw the rough...

technical field

[0001] The invention relates to an image clustering method, in particular to a medical image clustering 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 Imaging, MRI), medical images are playing an increasingly important role in clinical medical diagnosis.

[0003] Cluster analysis is one of the main techniques of data mining, and it is an unsupervised learning process. According to some attributes of things, it gathers things into classes, so that the similarity within a class is as small as possible, and the similarity between classes is as large as possible. Cluster analysis has been widely used in many fields, including pattern recognition, data analysis, image processing, and market research. Using cluster analysis to classify target objects in a single image is called image clustering. Ima...

Examples

Embodiment Construction

[0038] The medical image clustering method will be further described below mainly in conjunction with the accompanying drawings and specific embodiments.

[0039] In this embodiment, the brain MRI map is selected for analysis, and the original brain MRI containing noise points is selected. figure 2 a to illustrate the corresponding results after the implementation of the present invention, the concrete steps are as follows:

[0040] A. The computer reads the original image of the brain MRI map, and obtains the interval value of the image pixel point through the relationship between the image pixel point and its surrounding 8 neighborhood information, and uses the interval value each of the image like

[0041] B. According to the rough set principle, the left interval of each pixel interval value x N and the right interval Approximate into upper approximation and lower approximation, calculate the roughness ρ of each interval value A (x k ).

[0042] C. Draw the rough...

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