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A Wavelet approach to extract main features from indirect immunofluorescence images

2019

A number of previous studies have shown that IIF image analysis requires complex and sometimes heterogeneous and diversified methods. Robust solutions can be proposed but they need to orchestrate several methods from low-level analysis up to more complex neural networks or SVM for data classification. The contribution intends to highlight the versatility of Wavelet Transform (WT) and their use in various levels of analysis for the classification of IIF images in order to develop a system capable of performing: image enhancement, ROI segmentation and object classification. Therefore, WT was adopted in the de-noise section, segmentation and classification. This analysis allows frequencies cha…

Computer scienceData classificationWavelet Transform02 engineering and technologyPattern Recognition030218 nuclear medicine & medical imaging03 medical and health sciencesSegmentation0302 clinical medicineWaveletRobustness (computer science)IIF dataset0202 electrical engineering electronic engineering information engineeringSegmentationMedical diagnosisSettore INF/01 - InformaticaArtificial neural networkbusiness.industryDenoiseWavelet transformPattern recognitionClassificationSupport vector machine020201 artificial intelligence & image processingArtificial intelligencebusinessProceedings of the 20th International Conference on Computer Systems and Technologies
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