System for identifying focal liver lesions based on contrast-enhanced ultrasonography

Abstract

The invention discloses a system for identifying focal liver lesions based on contrast-enhanced ultrasonography. The system includes a video acquisition module, an image extraction module, an image pre-processing module, a classification labeling module, a feature extraction module, a data processing module, and a data output module, a mode based on combination of edge enhancement and color balance is used firstly for performing denoising and enhancement processing on a contrast image to enhance useful information of the image while retaining the color of the contrast image, then based on a gray level histogram of the image, feature extraction is performed on the contrast image to obtain reasonable quantitative parameters, so that the diagnostic errors caused by probe movement and lesion movement caused by breathing movements of a patient can be inhibited to the greatest extent, so that the features of the lesion is reflected scientifically, objectively, clearly, and effectively, so that accuracy rate and efficiency of feature identification of the focal liver lesions by a contrast-enhanced ultrasonography technology is improved, and thus a novel way is provided for the faster andbetter development of the contrast-enhanced ultrasonography technology.

Description

technical field [0001] The invention relates to the field of ultrasound technology, in particular to a system for identifying focal liver lesions based on contrast-enhanced ultrasound. Background technique [0002] Focal liver lesions are one of the common clinical liver lesions. Patients affected by the disease will continue to show symptoms such as liver function decline, and will cause serious damage to liver function after the disease progresses. Because most patients with focal liver lesions lack obvious lesions, it is difficult to find typical early symptoms, which makes misdiagnosis and missed diagnosis more likely to occur after seeing a doctor, which endangers the timing of effective treatment and even causes the disease to progress. endanger the health of the patient. Therefore, the most effective means of treating liver cancer is early detection and early treatment. Although CT, MRI, and especially PET imaging are effective in the early diagnosis of liver cancer...

AI Technical Summary

Problems solved by technology

In addition, the human visual system has inaccurate and uncertain defects in processing information, resulting in subjective differences in the recognition of contrast image features by different image analysts

In addition, when the number of recognition times is large, image analysts will inevitably experience visual fatigue, slow response, etc., and the error rate will increase.

At the same time, due to the data specifications, resolutions, and sizes collected by different devices, it is difficult to achieve uniformity, and when different doctors use the device probe to obtain images of contrast motion sequence

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