Feature classification method for liver ultrasonic image

Abstract

The invention provides a feature classification method for a liver ultrasonic image. The method comprises the steps of S1, for a to-be-processed ultrasonic image comprising a liver section / part, automatically extracting a liver capsule line from the ultrasonic image; S2, based on the extracted liver capsule line, selecting multiple sampling points, and generating a triple feature of each samplingpoint; S3, extracting each triple feature, and classifying the extracted triple features; and S4, according to a classification result of all the extracted triple features, determining the type of theultrasonic image. According to the method, classification results of all image blocks are combined to obtain an accurate classification result; the noise interference is reduced; automatic identification and classification are realized; and the labor cost is reduced.

Description

technical field [0001] The invention relates to image image analysis technology, in particular to a method for feature classification of liver ultrasound images. Background technique [0002] The liver is an organ in the body that mainly has metabolic functions, and it plays the roles of deoxidation, storage of glycogen, and synthesis of secreted proteins in the body. [0003] Liver cirrhosis is a common clinical chronic progressive liver disease, which is caused by diffuse liver damage caused by one or more pathogenic factors such as viral hepatitis, chronic alcoholism, malnutrition, and intestinal infection for a long time or repeatedly , can be complicated by splenomegaly, ascites, edema, jaundice, esophageal varices, hemorrhage, hepatic coma, can develop into liver cancer, and has a high mortality rate. [0004] Timely detection and treatment with drugs can delay the progress of liver cirrhosis, reduce the incidence of liver cancer, improve the long-term survival rate, ...

AI Technical Summary

Problems solved by technology

[0009] Compared with ultrasonography, other imaging methods have more or less disadvantages: traditional X-ray imaging lacks sufficient contrast resolution for the evaluation of liver cirrhosis, and its value is limited; the spatial resolution of CT technology is insufficient, so it cannot It can better distinguish the connective tissue of the liver parenchyma and has radiation damage; MRI has multi-plane imaging capabilities and higher soft tissue resolution, and is suitable for evaluating superficial organ tissue lesions, but it cannot perform real-time dynamic inspection, which is inconvenient and expensive

However, the texture features extracted from ultrasound images do not correspond to clinical diagnosis, and the classification accuracy cannot be guaranteed. Therefore, how to better extract the texture features of ultrasound images and improve the classification accuracy has become a problem that needs to be solved at present.

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