Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis

Dirk-Jan Kroon, Erik van Oort, Cornelis H. Slump

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Abstract

This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel intensities, histogram and MS probability atlas information. Principal Component Analysis(PCA) with log-likelihood ratio is used to classify each voxel. MRI suffers from intensity inhomogenities. We try to correct this 'bias field' with 3 methods: a genetic algorithm, edge preserving filtering and atlas based correction. A large observer variability exist between expert classifications, but the similarity scores between model and expert classifications are often lower. Our model gives the best classification results with raw data, because bias correction gives artifacts at the edges and flatten large MS lesions.
Original languageEnglish
Number of pages14
Publication statusPublished - 6 Sep 2008
Event11th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2008 - New York, United States
Duration: 6 Sep 200810 Sep 2008
Conference number: 11

Conference

Conference11th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2008
Abbreviated titleMICCAI
CountryUnited States
CityNew York
Period6/09/0810/09/08

Keywords

  • EWI-14856
  • Multiple Sclerosis
  • PCA
  • METIS-256141
  • Classifier
  • MRI
  • Image Processing
  • IR-65287

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