Stent marker detection using a learning based classifier in medical imaging
View Patent ↗Stent marker detection is automatically performed. Stent markers in fluoroscopic images or other markers in other types of imaging are detected using a machine-learnt classifier. Hierarchal classification may be used, such as detecting individual markers with one classifier and then detecting groups of markers (e.g., a pair) with a joint classifier. The detection may be performed in a single image and without user indication of a location.
1. A method for marker detection in fluoroscopic imaging of a stent, the method comprising:
receiving, from a fluoroscopic scanner, a fluoroscopic scan of a patient;
masking the fluoroscopic scan of the patient to limit the number of locations to which marker detection is applied;
individually detecting a plurality of possible markers for a stent in one frame of fluoroscopic data from the fluoroscopic scan without information from other frames of fluoroscopic data from the fluoroscopic scan, the individual detecting being with a first machine-trained classifier, wherein the first machine-trained classifier does not detect masked out locations;
jointly detecting a pair of markers from the plurality of possible markers for the stent output from the individually detecting, the joint detecting being with a second machine-trained classifier, wherein the second machine-trained classifier does not detect masked out locations; and
outputting an image representing information about the pair of markers.
2. The method of claim 1 wherein individually detecting comprises detecting with the first machine-trained classifier comprising a probabilistic boosting tree classifier.
3. The method of claim 1 wherein individually detecting comprises detecting with first input features comprising Haar features and wherein jointly detecting comprises detecting with second input features comprising gradient features.
4. The method of claim 1 wherein individually detecting further comprises removing some of the possible markers with a third machine-trained classifier, the third machine-trained classifier operating on an output of the first machine-trained classifier and the second machine-trained classifier operating on an output of the third machine-trained classifier.
5. The method of claim 1 wherein jointly detecting comprises fitting a rectangular box defining a region over the pair of markers and detecting as a function of an input feature set comprising a gradient magnitude, a gradient angle, or both relative to the rectangular box.
6. The method of claim 1 wherein jointly detecting comprises detecting with the second machine-trained classifier comprising a probabilistic boosting tree classifier.
7. The method of claim 1 wherein individually detecting comprises determining a first score for each of the possible markers, and wherein jointly detecting comprises determining a second score for each of paired combinations of the possible markers;
further comprising calculating a third score for each of the paired combinations, the third score being a function of the first and second scores associated with the possible markers in the paired combination, the pair of markers being the paired combination with a highest score.
8. The method of claim 1 further comprising:
steerably filtering the frame of fluoroscopic data such that guide wire regions are distinguished from background; and
wherein individually detecting comprises sampling locations along the guide wire regions.
9. The method of claim 1 further comprising:
repeating individual and joint detecting for a plurality of frames of fluoroscopic data;
aligning the frames of fluoroscopic data as a function of the respective pairs of markers; and
combining the aligned frames into a stent frame of data; and
wherein outputting the image comprises outputting the image as a function of the stent frame of data.
10. The method of claim 1 wherein outputting the image comprises outputting an image comprising a fluoroscopic image with the pair of markers highlighted.
11. The method of claim 1 further comprising:
selecting a marker reference frame of data from a plurality of frames of fluoroscopic data, including the frame of fluoroscopic data, the selecting being as a function of a distance between markers in the pair of markers for each of the plurality of the frames and as a function of probabilities of the pair of markers being correct.