Brain MR medical image registration method

Abstract

The invention discloses a brain MR medical image registration method. The method comprises the steps of 1, segmenting a reference image and a floating image by adopting a BCFCM method; 2, performing symmetry axis detection on the original reference image and the original floating image by adopting an MSR detection method, and extracting a symmetry axis equation; 3, performing threshold segmentation on the image processed in the step 1; 4, performing approximate symmetry constraint on the image processed in the step 3 according to the symmetry axis equation detected in the step 2, and marking the obtained image as a reference image and a floating image to be registered; 5, initializing rigid body transformation matrix registration parameters; and 6, registering the image through an SSD similarity measurement criterion to obtain an optimal transformation matrix registration parameter, segmenting the reference image and the floating image by using a BCFCM method; and performing binarization threshold processing on the segmented image to separate the background before separation, performing symmetry constraint on the image after binarization processing, and applying the image after binarization processing to multi-modal image registration under an SSD framework. The registration efficiency, the precision and the robustness are improved.

Description

technical field [0001] The invention relates to a brain MR medical image registration method. Background technique [0002] Image registration is the process of matching two or more images of the same scene or the same target at different acquisition times, different sensors, and different acquisition conditions. At present, image registration technology has been widely used in remote sensing image processing, medical image processing and other fields. [0003] For multimodal brain MRI image registration, it is necessary to match the floating brain MR image with the reference brain MR image (the floating image is different from the reference image modality). In addition, each modality image has its own limitations such as uneven gray scale and being affected by external noise. How to accurately register the brain MR floating image with the reference image has become a problem to be solved. [0004] Existing technologies mainly include pixel-level registration and feature-l...

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Problems solved by technology

[0006] Due to the problems of uneven gray scale and instrument noise in medical images, the effect of using k-means method to segment medical images is poor

For tissues and organs whose gray value in the image is similar to the background, it is easy to miss detection; for noise with sudden changes in gray value in the image, it is easy to miss detection

[0007] In addition, since the mutual information measurement needs to count the pixel values ​​of the registered image and the image to be registered and calculate the gray histogram, and calculate the mutual information measurement value according to the gray histogram, and further image registration, the algorithm measurement function is relatively Complicated, long registration time, low efficiency

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