Rapid robustness auto-partitioning method for abdomen computed tomography (CT) sequence image of liver

Abstract

The invention discloses a robustness auto-partitioning method for an abdomen computed tomography (CT) sequence image of a liver. The robustness auto-partitioning method comprises a data inputting step : in which a CT sequence to be partitioned is input and an initial slice is designated; a model building step in which a liver brightness model and an appearance model are built according to data characteristics of the input sequence,a complex background is suppressed and a liver region is highlighted; and an automatic partitioning step in which the initial slice is rapidly and automatically partitioned through combining the brightness model and the appearance model by a graph cut algorithm, and all slices in the liver CT sequence are iteratively partitioned upwards and downwards by taking the initial partition slice as a starting point according to spatial correlations between adjacent slices. According to the method, the corresponding brightness and appearance models are built with regards to the particular CT sequence, and thus, the liver with a low partitioning contrast ratio, boundary fuzziness and shape irregularity can be effectively and automatically partitioned. Moreover, the auto-partitioning method for the abdomen CT sequence image of the liver can be promoted to automatic partitioning of other abdominal organs, such as partitioning of the abdomen CT sequence image of a spleen and a kidney.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the segmentation of organs in abdominal CT sequence images, in particular to the fast and robust automatic segmentation of liver in abdominal CT sequence images, which can be used for medical image auxiliary diagnosis and treatment. Background technique [0002] Liver segmentation is a prerequisite for computer-aided diagnosis of liver diseases and preoperative planning for liver transplantation. The liver model obtained by segmentation and reconstruction can assist in liver lesion analysis, volume measurement, blood vessel analysis, liver segmentation, disease diagnosis and evaluation, etc. CT angiography images are generally favored by doctors because of their high resolution, little damage to the human body, and their ability to vividly and accurately reflect the location of the liver and its lesions. Due to the large number of image slices used in 3D imaging (about 20...

AI Technical Summary

Problems solved by technology

The purely image-based segmentation method refers to the method of directly using brightness, texture and other image information for segmentation, mainly including threshold method, clustering, region growing, active contour model and graph cut, etc. These segmentation techniques mainly have the following disadvantages and Disadvantages: (1) Complex preprocessing is required, including removal of surrounding tissues or organs such as ribs, spine, and kidneys; (2) It is difficult to segment CT sequence images with low contrast and blurred liver boundaries

The method based on the statistical model first uses a large number of CT sequence images to construct the target prior model, and then applies it to the segmentation of the current sequence. This type of method has a better segmentation effect for images with low contrast, but for irregular liver The segmentation effect is poor, it takes a long time, and it is sensitive to data initialization and registration

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