Probability density weighted geodesic distance-based brain MR image segmentation method

Abstract

The invention discloses a probability density weighted geodesic distance-based brain MR image segmentation method, and belongs to the technical field of image processing. The method comprises the steps of reading a plurality of simulated brain database images, and performing histogram statistics to obtain sample values of a most intensive distribution interval; performing probability density estimation on each pixel point on the selected to-be-processed image by using the obtained sample values as priori knowledge; performing super-pixel segmentation on the to-be-processed image based on a probability density function; scanning the segmented super-pixels, screening out the super-pixels which do not meet the standards to perform splitting, re-clustering all pixels in the super-pixels into two classes by using an FCM algorithm, searching for connected regions according to classification results, taking the pixels in each connected region as a new class, and updating a super-pixel classification result matrix; and performing clustering based on all the updated super-pixels by using the FCM algorithm to obtain a brain tissue segmentation result of the to-be-processed image. According to the method, the accuracy of super-pixel segmentation and brain tissue segmentation is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a brain MR image segmentation method based on probability density weighted geodesic distance. Background technique [0002] Image segmentation is one of the most classic research topics in the fields of image processing, image analysis, and computer vision, and it is also one of the biggest difficulties. Image segmentation technology plays a key role in many medical image applications, and it is also an important part of various tissues and organs in images. The basis for further analysis of the pathology of the human body, through the use of image segmentation, the region of interest in the image is extracted to provide a basis for clinical diagnosis and treatment, and the brain is an important organ of the human body, so the study of brain region segmentation technology is very important for the brain It is of great significance for the three-dimensional reconstruction...

AI Technical Summary

Problems solved by technology

However, the traditional FCM algorithm fails to consider the grayscale features of each point and the correlation degree of its neighboring pixels in image segmentation, which leads to the algorithm being sensitive to noise and grayscale inhomogeneity. Aiming at the above problems, the proposed Many improved FCM algorithms, although the improved method has a certain degree of improvement in anti-noise or efficiency, but due to the high complexity of brain images, satisfactory segmentation results cannot be achieved, so the traditional single method segmentation cannot meet actual requirements

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