CT image liver segmentation method and system based on multi-scale weighting similarity measure

Abstract

The invention discloses a CT image liver segmentation method and system based on multi-scale weighting similarity measure and capable of accurately segmenting a liver area. The method comprises steps of: S101, reading a training image set and a to-be-segmented image; S102, preprocessing the read image data; S103, extracting superpixels from an area around an initial bound and a liver bound in the to-be-segmented image; S103, by using the central point of each superpixel in the to-be-segmented image as a center, selecting all pixels within a certain neighborhood as test blocks and selecting multi-scale image blocks with the same positions and sizes from training images as training blocks to obtain a training block set; S105, computing the similarity measure between the test blocks and the training block set to obtain the prior probability that each superpixel around the liver bound in the to-be-segmented image belongs to the liver; and S106, modifying a randomly moving graph model weight value in combination with a prior model and the to-be-segmented image so as to segment the liver in the to-be-segmented image.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a CT image liver segmentation method and system based on multi-scale weighted similarity measure. Background technique [0002] Medical image segmentation assists doctors in identifying patients' internal tissues, organs and lesion areas, and plays a vital role in computer-aided treatment and surgical planning. Therefore, the automatic segmentation of the liver is the basis for doctors to diagnose and treat liver diseases such as cirrhosis, liver tumors, and liver transplantation. In abdominal CT images, the gray value difference between the liver and adjacent organs is small, and the liver itself has uneven gray levels and different shapes, so it is difficult to automatically and accurately segment the liver. Therefore, clinicians urgently need a simple, fast and accurate liver segmentation method. [0003] Existing liver segmentation methods include: (1) Threshold-ba...

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Problems solved by technology

However, the threshold-based segmentation method is more dependent on the selected initial threshold, and there is a serious over-segmentation problem for the liver with blurred boundaries; (2) The method based on the shape model, but the difficulty of the method based on the shape model lies in the establishment of the model. This not only requires a large number of training sets, but also requires the corresponding relationship between the shape models; (3) the method based on graph theory, the method based on graph theory is relatively fast and simple, but the effect of image segmentation with insignificant difference in gray value is relatively poor. (4) The learning-based method, the difficulty of the learning-based method lies in the selection of features, and the learning is often at the pixel level, the recognition rate of the liver is low for the method only relying on learning

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