Liver ultrasonic image identification method based on sparse expression
An ultrasound image and sparse representation technology, which is applied in the field of liver ultrasound image recognition based on sparse representation, and can solve the problems of complex and changeable space-occupying lesions.
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2016-09-21
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
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Abstract
Description
technical field
[0001] The invention relates to the technical field of ultrasonic image processing, in particular to a liver ultrasonic image recognition method based on sparse representation. Background technique
[0002] Liver cancer ranks sixth in cancer incidence and third in mortality worldwide. Early diagnosis of liver disease is conducive to the early detection and control of liver cancer, improving the survival rate of patients. Ultrasonography has the characteristics of no radiation, simple operation, repeatability, and low cost, so it is widely used in clinical diagnosis of liver diseases. Clinically, the diagnosis of ultrasound images of liver lesions relies on the naked eye observation of doctors to identify them. Not only is the workload huge, but the level of diagnosis depends to a certain extent on the experience of doctors. Therefore, it is of great significance to improve the overall level of ultrasound diagnosis by using medical image processing technolog...
Examples
Embodiment
[0068] As shown in Figure 1, a liver ultrasound image recognition method based on sparse representation includes the following steps:
[0069] (1) Select the region of interest from the liver ultrasound image training sample with the space-occupying lesion region, the region of interest includes the space-occupying lesion region R 1 and normal liver area R 2 The liver ultrasound image training samples include liver cyst image samples, hepatic hemangioma image samples, and liver cancer image samples, specifically including the following steps:
[0070] (1-1) Select the space-occupying lesion area R 1 : First, use the region-growing ultrasonic image automatic segmentation algorithm based on energy constraints to outline the edge of the lesion area, then take its circumscribed rectangle, and use the circumscribed rectangle area as the occupying lesion area R 1 ;
[0071] ROI (region of interest) refers to a region selected from the image, which is the focus of image analysis. ...