Breast cancer is a cancer which occurs commonly for women of the entire world.[9] In this thesis, in order to find cancer by using image conformation technology, computer tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) images were used. For conforming the images of each the different modalities, surface points, which become common feature of MRI and CT images were extracted and they were registered in 3D by using iterative closet point. (ICP)
The objective of this study is to develop a 3D breast image registration algorithm for PET-CT and MR system. Proposed algorithm consists of three stages: breast segmentation, surface matching, and image transformation. Contrast-enhanced MR image volume was used as a reference and CT volume was transformed with varying parameters to calculate similarity between two image modalities. At first, the breast regions were cropped separately by the pre-determined regional masks. For the CT image case, region growing based breast segmentation was explored to eliminate the breast dedicated jig which has been developed to hold the shape of breast during PET-CT scan. After the segmentation, each of the points set of breast surface has been extracted. The extracted point sets were used as a feature vector for the surface matching. For the surface matching, we developed
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