Method and device for automatically detecting boundary of beam-limiting device

Abstract

The invention discloses a method and a device for automatically detecting the boundary or region of beam-limiting device. The method mainly comprises a reading step for reading an image acquired through an X-ray imaging system, a detection step for detecting transition points between a foreground and a background as well as between the foreground and an organization on the X-ray image, and a transformation step for detecting boundary straight lines between the foreground and the background as well as between the foreground and the organization by utilizing the detected transition points through HT or RT transformation. The detecting method and the detecting device have the advantages of simple calculation, high detection ratio and capability of effectively solving detection omission and false positive problems.

Description

technical field [0001] The invention relates to an X-ray image processing method and device, in particular to a method and device for automatically detecting the boundary or region of a beam limiter in an X-ray image. Background technique [0002] In an X-ray image, the beam limiter (Collimator, also referred to as beam filter, shutter, etc.) area is usually called the foreground (Foreground). The foreground image is useless information. In the enhancement processing of X-ray images, it is often necessary to remove the pixels in this area to avoid the influence of useless information on image enhancement processing, so that the enhancement effect is better and the image quality is better. Limiter clipping can be divided into automatic clipping and manual clipping. However, manual cropping has certain disadvantages. On the one hand, manual cropping increases the workload of clinical radiology technicians, thereby reducing the efficiency of clinical radiographs; on the other ...

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Problems solved by technology

[0006] From the above introduction, it is not difficult to see that the problems existing in the existing technology are: the algorithm is too complex and the operation speed is slow; the accuracy rate needs to be further improved, for example, the transition boundary between bone and soft tissue may be mistaken for the beam limiter area The boundary of the boundary; it is necessary to have more accurate background pixel threshold information to verify whether each detected boundary line is valid and to verify whether the detected overall beam limiter boundary is valid; for underexposed images with poor contrast, or overexposure images may not be very effective and false positives may occur

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