Automatic precise partition method for prostate ultrasonic image

Abstract

The invention discloses an automatic precise partition method for a prostate ultrasonic image and belongs to the field of computer-aided diagnosis. The method comprises the following steps of: extracting the textural features of the prostate ultrasonic image under different scales through Gabor by using a multi-scale space constructed by anisotropic diffusion; meanwhile, constructing a prostate shape space by a non-parameter kernel density estimation method, and searching in the shape space through mean shift; under the dual constraints of the textural features and the shape space, roughly partitioning a prostate contour; and finally, refining a partition result in a self-adaptive detection range by using active contour models in combination with orientation gradients, and accurately partitioning the prostate ultrasonic image stably at last. By the method, the problems of low contrast ratio of ultrasonic images and large interference of speckle noise and shadow areas in partition are solved; the prostate ultrasonic image can be accurately partitioned; and the method can adapt to ultrasonic machines which are produced by different manufacturers and have different models, and is not sensitive to the parameter setting of an ultrasonic imaging system.

Description

technical field [0001] The invention relates to the field of computer aided diagnosis, in particular to image segmentation technology. Background technique [0002] Ultrasonic Medicine (Ultrasonic Medicine) is an important part of Medical Imageology (Medical Imageology), together with CT, MRI, and Radioisotope Scan (Radioisotope Scan), it is called the four main inspection methods of medical imaging diagnosis. [0003] The accurate extraction of the contour of the prostate is the basis for the computer to participate in a series of auxiliary analysis. However, manual boundary extraction is very time-consuming. At the same time, limited by human experience, relevant knowledge and attention, the analysis results may be variable and inaccurate. Therefore, using computer to automatically extract the contours of prostate ultrasound images not only has theoretical research value, but also has very important significance for promoting the development of medical technology, early d...

AI Technical Summary

Problems solved by technology

This method uses unpreprocessed images without further mining the information contained in the image (such as texture characteristics)

At the same time, the signal-to-noise ratio and contrast of ultrasound images are low, and it is difficult to obtain better processing results directly on the original image.

In addition, this method also has certain limitations in modeling the target shape, because Principal Component Analysis (Principal Component Analysis) can only remove the linear correlation of different dimensions in the training set, and it is difficult to completely remove the general correlation that exists in real data. It can simulate it well when the training set is very close to the Gaussian distribution, but it is difficult to achieve good results for some complex distributions

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