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Languages: English
Types: Unknown
Subjects: RC0254, QA76

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Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • 2. Lessmann, B., Hans, V., Degenhard, A., Nattkemper, T.W.: Feature space exploration of pathology images using content-based database visualization. In: Proceedings SPIE Medical Imaging. (2006)
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    • 4. Wirjadi, O., Breuel, T., Feiden, W., Kim, Y.J.: Automated feature selection for the classi¯cation of meningioma cell nuclei. In Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdor®, T., eds.: Bildverarbeitung fr die Medizin. Informatik Aktuell, Springer (2006) 76{80
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