Method for automatically identifying liver tumor type in ultrasonic image

Abstract

The invention relates to the field of medical image processing, in particular to a method for automatically identifying a liver tumor type in an ultrasonic image. The method can provide auxiliary diagnosis when a doctor diagnoses a liver lesion according to a CEUS medical image, wherein the auxiliary diagnosis comprises the process of giving out a lesion identification result, the time when the lesion is relatively remarkable and a position where the lesion is relatively remarkable. The method specifically comprises: representing a CEUS image with a plurality of regions of interest (ROI); distinguishing different lesions by expressions and changes of the ROI in time and space; and representing a space-time relationship among the ROI by establishing models in time and space at the same time, wherein the models can determine relatively proper ROI and related parameters of the models according to existing CEUS lesion samples with an iterative learning method. After a sample is given out, the proper ROI can be determined by removing part of improper ROI in advance and a fast search method, and reference diagnosis is given out for the lesion.

Description

Technical field [0001] The present invention relates to the field of medical image processing, and more specifically, to a method for automatically identifying liver tumor types in ultrasound images. Background technique [0002] Liver tumors are the fifth most common tumor and the second leading cause of death in cancer. Focal liver lesions (FLLs) are abnormal solid or cystic masses in the liver. The early detection and diagnosis of FLLs in liver cancer are of great significance to the treatment of liver cancer. In the process of diagnosis, medical imaging plays a very important role. Especially in recent years, with the development of imaging technology, medical imaging has become more and more important in diagnosis. Medical imaging includes electronic computed tomography (CT), magnetic resonance imaging (MRI), ultrasound imaging (US), etc., among which CT and MRI require high costs, complicated equipment, and CT imaging can also cause ionizing radiation. Ultrasound imaging ...

AI Technical Summary

Problems solved by technology

Determining the location of the lesion is a very important step in the identification of this method, but the above methods still fail to identify focal liver lesions

There is still no direct identification of lesion types in the medical field

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