Medical image segmentation algorithm used in CT room

Abstract

The present invention discloses a medical image segmentation algorithm used in a CT room. The segmentation steps of the algorithm are that: firstly, a CT image is segmented into different small areas via a watershed algorithm, then an average gray value of the small areas is mapped to a high dimension characteristic space according to a KFCM algorithm and by utilizing a Mercer kernel, so that the original characteristics which are not displayed in a watershed algorithm segmentation image are displayed. The algorithm comprises the following steps of (1) pre-processing an image, and carrying out the median filtering; (2) carrying out the watershed segmentation on the pre-processed image, and storing the label k of each small area; (3) calculating the average gray value x<k> of each area, wherein the average gray value x<k> represents a sample set of an input space, k =1, 2, ..., n, n is the area number formed after the image watershed segmentation; (4) selecting a classification number C, a threshold value epsilon and a fuzzy exponent m; (5) calculating a clustering center and a weighting matrix. The medical image segmentation algorithm used in the CT room utilizes the advantages of the watershed algorithm and the weighted kernel clustering, also greatly overcomes the disadvantages of the two algorithms.

Description

technical field [0001] The invention relates to the field of CT technology, in particular to a medical image segmentation algorithm used in a CT room. Background technique [0002] Image segmentation refers to separating the meaningful objects in the image from their background, and separating these objects according to different meanings. The methods can be roughly divided into two categories: edge detection-based methods and region-based methods. Currently, CT Imaging technology is widely used in visceral imaging, bone detection and other human tissue visualization, which can provide detailed images of various parts of the human body in any plane. However, due to the influence of various external factors such as noise in the CT imaging process, aliasing and blurring between different tissues are prone to occur, resulting in difficulties in image segmentation. Wang Na et al. proposed a new type of FCM method, which is a fuzzy clustering method, suitable for medical images ...

AI Technical Summary

Problems solved by technology

However, due to the influence of various external factors such as noise in the CT imaging process, aliasing and blurring between different tissues are prone to occur, resulting in difficulties in image segmentation.

Wang Na et al. proposed a new type of FCM method, which is a fuzzy clustering method, suitable for medical images such as MRI or CT with uncertainty and ambiguity in segmentation, and is an effective medical image segmentation method. Segmentation method, however, the FCM algorithm does not optimize the characteristics of the sample, but directly uses the characteristics of the sample to optimize, but directly uses the characteristics of the sample to cluster, so that the clustering is easily subject to the distribution state of the sample