Target detection method and device for CT image data and electronic equipment

Abstract

The invention provides a target detection method and device for CT image data, and electronic equipment, which relate to the technical field of image processing. The method comprises the steps of: obtaining the CT image data, the CT image data comprising target features, performing pixel normalization on the CT image data to obtain a plurality of grayscale images, extracting a grayscale image group from the plurality of grayscale images according to a preset method, and combining the grayscale images into a corresponding pseudo-color image, the grayscale image group being composed of grayscaleimages according to a specified number and a specified sequence, and inputting the pseudo-color image into a pre-trained neural network, so that the pre-trained neural network detects the pseudo-color image to obtain the category and the position of the target feature. Therefore, the accuracy of CT image data target detection can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a target detection method, device and electronic equipment for CT image data. Background technique [0002] Due to the three-dimensional properties of some CT image data, the amount of information is huge and the structure is complex, most of the existing technologies use image processing methods such as edge detection, morphology, etc. to process the input image through artificially designed features, which cannot be effectively Using a large amount of annotation information, supervised learning and poor detection generalization performance for target features. In addition, the target detection of CT image data can only detect a specific target, and cannot detect multiple target features, resulting in low accuracy of target detection in CT image data. [0003] For the above-mentioned problem of low accuracy of object detection in CT image data, no effective sol...

AI Technical Summary

Problems solved by technology

[0002] Due to the three-dimensional properties of some CT image data, the amount of information is huge and the structure is complex, most of the existing technologies use image processing methods such as edge detection, morphology, etc. to process the input image through artificially designed features, which cannot be effectively Using massive labeling information for supervised learning and poor detection generalization performance for target features

In addition, the target detection of CT image data can only detect a specific target, and cannot detect a variety of target features, resulting in low accuracy of target detection in CT image data

[0003] The above-mentioned problem of low accuracy of target detection in CT image data has not yet proposed an effective solution

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