A multi-dimension and multi-feature extraction method for medical images

Abstract

The invention provides an extraction method of multiple multi-dimensional features of carotid artery images. The method particularly includes the steps that two-dimensional sequence images and three-dimensional images in an area of interest are built according to medical three-dimensional ultrasound volume data of the carotid artery; two-dimensional features of the two-dimensional sequence images are extracted, and three-dimensional features of the three-dimensional images are extracted, wherein the two-dimensional features comprise two-dimensional texture features, two-dimensional shape features and two-dimensional elastic features, and the three-dimensional features comprise three-dimensional texture features; whether the features make contributions to image classification or not is verified, and the features which make contributions to image classification are reserved; the objective is to achieve a minimum false discovery rate, and the features which make contributions to image classification are looked up to acquire a global optimal feature combination. The multiple multi-dimensional features, including textures, shapes and elasticity, of the images are extracted, the number of the features is multiple, the types of the features are complete, image characteristics can be completely, objectively and accurately reflected, classification accuracy is improved, and an important reference basis is provided for clinical application.

Description

technical field [0001] The invention belongs to the field of medical image information processing, and in particular relates to a method for extracting multidimensional and various features of medical images. Background technique [0002] Image feature extraction is becoming one of the research hotspots in the field of computer-aided diagnosis, which is of great significance. For example, in carotid atherosclerosis, feature extraction is helpful for the automatic identification of vulnerable plaques, which has an effective auxiliary effect on doctors' clinical diagnosis. Atherosclerotic plaque rupture triggers thrombus, which is very likely to lead to acute cardiovascular events. However, not all plaques rupture and form a thrombus; whether a plaque ruptures depends on its vulnerability. Vulnerable plaque refers to plaque that is easily damaged, forms a thrombus, and may develop rapidly to cause coronary artery blockage and death. Therefore, identifying vulnerable plaques...

AI Technical Summary

Problems solved by technology

[0005] To sum up, previous studies mostly start from a single dimension or only extract a certain type of image features, which cannot fully and objectively reflect the characteristics of the image.

Therefore, the classification accuracy is low, the classification effect is poor, and the practicability is not strong

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