24 June 1998 Combining cluster analysis with supervised segementation methods for MRI
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This paper presents development and application of an automated scheme to minimize user dependency of the eigenimage filter. The steps of the new method are as follows: (1) User defines sample regions of interest on central location of the volume and generates the corresponding eigenimages. (2) Original images are segmented using a self organizing data analysis technique. (3) Regions for the tissue types are automatically found from the segmentation results. (4) Signature vectors are estimated from these regions and are compared with those obtained from the user initialization. If they are similar, these signature vectors are used to run eigenimage filtering. If they are not similar, the clustering parameters are adjusted and the procedure is repeated until similar signatures are found. (5) Next slice is loaded and signature vectors from the previous slice are used to get initial eigenimages. Cluster analysis is used to generate regions. The procedure described in the previous step is repeated until similar signatures are found. Then, final eigenimages are obtained. (6) Previous step is repeated until the last slice of the volume is analyzed. (7) Volume of each tissue type is estimated from the resulting eigenimages. Details and significance of each step are explained. Experimental results using simulation, phantom, and brain images are presented.
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Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Joe P. Windham, Joe P. Windham, Donald J. Peck, Donald J. Peck, Linda Emery, Linda Emery, } "Combining cluster analysis with supervised segementation methods for MRI", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310872; https://doi.org/10.1117/12.310872

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