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Adaptive Template Moderated Spatially Varying Statistical Classification
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Institution: |
1Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, 75 Francis St., Boston, MA 02115 USA 2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. |
Publisher: |
Med Image Comput Comput Assist Interv. MICCAI 1998 |
Publication Date: |
Oct-1998 |
Citation: |
Int Conf Med Image Comput Comput Assist Interv. 1998;1:231-238. |
Keywords: |
Template moderated segmentation, brain, elastic matching, nearest neighbour classification, neonate, knee cartilage, tumour |
Appears in Collections: |
SPL |
Generated Citation: |
Warfield S, Kaus M, Jolesz F, Kikinis R. Adaptive Template Moderated Spatially Varying Statistical Classification. Int Conf Med Image Comput Comput Assist Interv. 1998;1:231-238. |
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| Paper: | Download, View online |
| Export citation: |
A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy. The new algorithm is a form of spatially varying classification (SVC), in which an explicit anatomical template is used to moderate the segmentation obtained by k Nearest Neighbour (tex2html_wrap_inline378) statistical classification. The new algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which creates an adaptive, template moderated (ATM), spatially varying classification (SVC). The ATM SVC algorithm was applied to several segmentation problems, involving different types of imaging and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of babies, MRI of knee cartilage of normal volunteers) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumours, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone.
Additional Material
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Warfield-MICCAI1998-fig2.jpg (101.144kB)