A 3D Interactive Segmentation Method for MR Image Based on Active Contour Model Based on Random Walk and Graph Cut

Abstract

The invention discloses an MR (magnetic resonance) image three-dimensional interactive segmenting method for random walks and graph cuts based active contour model, comprising the steps of S1, selecting a seed point to capture local three-dimensional MR image data including pituitary adenomas in order to acquire a segmented initial boundary surface; S2, establishing a hybrid active contour model based on the initial boundary surface to obtain a model energy function; S3, discretizing the model energy function; S4, using each pixel of the captured local three-dimensional MR image as an image node, and using 6 neighborhoods of each pixel to construct an image; giving an initial value to each of the pixels inside and outside the initial boundary surface; giving a corresponding weight to each of sides connecting between the nodes, between the nodes and source points and between the nodes and meeting points according to the discretized energy function; S5, performing image segmentation computing based on the constructed mage to obtain segmentation results, and extracting boundary surface form the segmentation results to obtain a segmented contour; S6, replacing the initial boundary surface of S1 with the current segmented contour, and repeating the steps S2 to S5 until the segmentation results converge to obtain a final segmented contour. By using the method of the invention, it is possible to provide three-dimensional segmentation for MR images of pituitary adenomas, and provide more accurate segmentation for the images of pituitary adenomas.

Description

technical field [0001] The invention relates to the technical field of image segmentation, in particular to a three-dimensional interactive segmentation method of an MR image based on an active contour model of random walk and graph cut for area segmentation of a magnetic resonance imaging human brain image. Background technique [0002] Magnetic resonance imaging (MRI) has excellent soft tissue resolution and can image various parts of the human body from multiple angles and planes. The application of various imaging methods and pulse sequence technology is helpful for the localization and qualitative diagnosis of pituitary tumors . Usually, tumor segmentation in clinical medical images is done by doctors manually drawing the boundaries of pituitary tumors sequentially on two-dimensional images. According to the Response Evaluation in Solid Tumors (RECIST) standard, the radiologist will identify the high-brightness / low-brightness tissue of the brain on the two-dimensional ...

AI Technical Summary

Problems solved by technology

However, most pituitary tumors have irregular shapes and may infiltrate other brain tissues. Therefore, templates or shape models play a limited role in the segmentation of irregularly shaped pituitary tumors.

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