Compression and quality evaluation method for region of interest (ROI) of CT (Computed Tomography) image

Abstract

The invention provides a compression and quality evaluation method for the region of interest (ROI) of a CT (Computed Tomography) image. The method comprises ROI extraction, ROI compression and quality evaluation. The method comprises the following steps of defining the ROI according to CT image characteristics and human vision characteristics; extracting the ROI based on an image segmentation principle; marking the ROI and a non-ROI separately by adopting a MAXSHIFT algorithm; performing layered compressed encoding on the ROI based on DWT (Discrete Wavelet Transform) and EBCOT (Embedded Block Coding with Optimized Truncation); computing a structural similarity index matrix (SSIM) based on a human vision characteristic contrast sensitivity function (CSF) and contourlet transform (CT); and verifying ROI compressed image quality through CT-SSIM. The image segmentation principle is applied to the extraction of the ROI, so that the ROI comprising internal and external outline information can be extracted automatically and accurately, ROI compression is performed on a CT medical image, and a file is compressed while medical diagnosis information is kept. Secondly, through an objective quality evaluation method based on the human vision characteristics, a human vision evaluation result is approached to the maximum extent, and subjective evaluation can be replaced.

Description

technical field [0001] The invention belongs to the field of image compression and quality assessment, and relates to an image segmentation-based interest region layered compression and a CT-SSIM-based objective quality assessment method. Background technique [0002] There are many types of medical images and a large amount of data. Among them, CT acquisition, storage and transmission is not a single image file but a series, which consumes a lot of storage space and wireless transmission channels. In order to reduce the amount of data stored and transmitted, medical images need to be compressed. At present, the medical field performs lossless compression processing on images to avoid loss of medical evidence, but the compression ratio is limited. If a lossy compression algorithm is used, the compression ratio is large, but medical diagnosis data may be lost, causing medical disputes. [0003] In order to overcome the above shortcomings, the researchers proposed a layered ...

AI Technical Summary

Problems solved by technology

Subjective quality assessment is to judge the image quality through the subjective consciousness of the observer, which is inconvenient to operate and consumes a lot of time and energy

Traditional quality assessment methods are based on the difference between the pixel domain of the original image and the compressed image to calculate the score value. Only the calculation of the gray level difference of the image has limitations and cannot reflect the human visual evaluation results.

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