Abstract

Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical model for tumor and normal tissue. We show that using a conditional model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative models. Validation is performed by testing the method on several cancer patient MRI scans.

Keywords

SegmentationArtificial intelligenceComputer sciencePattern recognition (psychology)Image segmentationFeature (linguistics)Data setSimilarity (geometry)Generative modelSet (abstract data type)Process (computing)Computer visionImage (mathematics)Generative grammar

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Year
2007
Type
article
Pages
1-8
Citations
107
Access
Closed

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Dana Cobzaş, Neil Birkbeck, Mark Schmidt et al. (2007). 3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set. , 1-8. https://doi.org/10.1109/iccv.2007.4409130

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DOI
10.1109/iccv.2007.4409130