Abstract / truncated to 115 words (read the full abstract)

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerized histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each ... toggle 6 keywords

digital pathology – meningioma subtype classification – medical image analysis – wavelet transforms – pattern recognition – features extraction

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