Evolution strategy classification utilizing meta features and domain-specific statistical a priori models for fully-automated and entire segmentation of medical datasets in 3D radiology

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@InProceedings{Zwettler:2015:ICCCT,
  author =       "Gerald Zwettler and Werner Backfrieder",
  booktitle =    "2015 International Conference on Computing and
                 Communications Technologies (ICCCT)",
  title =        "Evolution strategy classification utilizing meta
                 features and domain-specific statistical a priori
                 models for fully-automated and entire segmentation of
                 medical datasets in {3D} radiology",
  year =         "2015",
  pages =        "12--18",
  abstract =     "The employment of modern machine learning algorithms
                 marks a huge advance towards automated and generalised
                 segmentation in medical image analysis. Entire
                 radiological datasets are classified, leading to a
                 meaningful morphological interpretation, clearly
                 distinguishing pathologies. After standard
                 pre-processing, e.g. smoothing the input image data,
                 the entire volume is partitioned into a large number of
                 sub-regions using watershed transform. These fragments
                 are atomic and fused together building contiguous
                 structures representing organs and typical morphology.
                 This fusion is driven by similarity of regions. The
                 relevant similarity measures respond to statistical
                 a-priori models, derived from training datasets. In
                 this work, the applicability of evolution strategy as
                 classifier for a generic image segmentation approach is
                 evaluated. Furthermore, it is analysed if accuracy and
                 robustness of the segmentation are improved by
                 incorporation of meta features evaluated on the entire
                 classification solution besides local features
                 evaluated for the pre-fragmented regions to classify.
                 The proposed generic strategy has a high potential in
                 new segmentation domains, relying only on a small set
                 of reference segmentations, as evaluated for different
                 imaging modalities and diagnostic domains, such as
                 brain MRI or abdominal CT. Comparison with results from
                 other machine learning approaches, e.g. neural networks
                 or genetic programming, proves that the newly developed
                 evolution strategy is highly applicable for this
                 classification domain and can best incorporate meta
                 features for evaluation of solution fitness.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICCCT2.2015.7292712",
  month =        feb,
  notes =        "Bio- & Med. Inf. Dept., Univ. of Appl. Sci. Upper
                 Austria, Hagenberg, Austria ; Also known as
                 \cite{7292712}",
}

Genetic Programming entries for Gerald Zwettler Werner Backfrieder

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