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The Study of Brain Tissue Segmentation Methods from MR Images with ITK

Author: JiangHong
Tutor: ZhangZhaoChen
School: Taishan Medical College
Course: Medical Imaging and Nuclear Medicine
Keywords: ITK MR brain tissue Segmentation Continuous threshold region growing K-Means Clustering
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 129
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Abstract


Objective ITK semi-automatic and automatic segmentation of brain MR images , and structure of the split from the brain tissue ( white matter , gray matter , ventricles ) image observation , research , analysis of the advantages and disadvantages of , for the three-dimensional structure of brain tissue in brain MR images show prepare and surgical navigation . Materials and methods using ITK programming read DICOM format MR images of the brain , using semi-automatic segmentation the continuous threshold region growing algorithm , automatic segmentation using K-Means clustering algorithm to split the 41 images , the white matter , gray matter , ventricles and brain structure . Results threshold region growing algorithm and K-Means clustering algorithm is very good split out the organizational structure of the various parts of the brain . The continuous threshold region growing algorithm for segmentation slow connectivity between different layers of different brain tissue pixels on MR images of the brain , brain tissue structure graded single display , compared to the K-Means does not contain ventricle the split high precision image classification more details . Speed ??faster than the region growing segmentation method segmentation K-Means clustering algorithm divided by the initial cluster centers clustering criterion function similarity measure method can be a one-time structure of the white matter , gray matter and ventricle in a segmentation on the images displayed, the segmentation results of the five categories of the image that contains the ventricle structure was significantly better than the continuous threshold region growing segmentation results, and such disposable each brain tissue structure on an image segmentation rapid , the efficient segmentation ideology and mode of the future direction of image segmentation . The conclusions of the advantages and disadvantages of the different segmentation method , the focus is different, plus medical images of the opposite sex, complex , multi - modal , as well as the diversity of the purpose and requirements of image segmentation , concreteness and particularity , the specific issues need specific research and analysis . Characteristics of each of the consecutive threshold region growing algorithm and the segmentation of the K-Means clustering algorithm , very good split out the organizational structure of the various parts of the brain , can , according to the need to apply them to the 3D display and surgery of the brain MR images of brain tissue structure navigation .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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