Abstract
In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.
Highlights
With the development of digital image processing technology, the application of 3D digital image processing technology to medical image analysis can improve medical pathological diagnosis and analysis capabilities
The recognition of 3D human brain CT images is based on threshold segmentation, and accurate localization of human brain diseases is achieved through image segmentation and edge contour feature extraction
It is of great importance to study threshold segmentation methods of multi-resolution 3D human brain CT image in recognition of human brain CT image, and more and more attentions have been paid to the research of image processing methods.[2,3]
Summary
With the development of digital image processing technology, the application of 3D digital image processing technology to medical image analysis can improve medical pathological diagnosis and analysis capabilities. In Ref. 5, a threshold segmentation method of multi-resolution 3D human brain CT image based on texture superpixel edge segmentation is proposed In this method, dynamic enveloping contour decomposition method is adopted to perform block segmentation of CT images, and the feature matching method is adopted to reconstruct and recognize the dynamic feature points of the 3D human brain CT images, so as to improve the pixel matching ability of the images. In Ref. 6, a feature segmentation algorithm of 3D human brain CT image based on bright spot distribution detection is proposed In this method, an active contour model for 3D human brain CT images is constructed, and threshold denoising andltering are performed on the images, and with the corner detection method, the abrupt point detection of the CT images is performed, which improves the ability to rapidly search and locate the disease cause points of the CT images. Pixel Feature Acquisition and Denoising Preprocessing of 3D Human Brain CT Image
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