Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Texture classification using wavelet transform
Pattern Recognition Letters
White blood cell segmentation using morphological operators and scale-space analysis
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Expert Systems with Applications: An International Journal
Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
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This paper investigates an image classification method performing thyroid carcinoma classification in Fine Needle Aspiration Biopsy cytological images of thyroid nodules under noise conditions and varying staining conditions. The segmentation method combines the image processing techniques thresholding and mathematical morphology. Feature extraction and classification are carried out by discrete wavelet transform and Euclidean distance based on k-nearest neighbor classifier, respectively. The classification methodology is successfully tested for Papillary carcinoma and Medullary carcinoma cytological images of thyroid nodules, showing promising results, encouraging future research work. The maximum classification rate of 95.84% and minimum classification rate of 79.17% have been reported for various testing sets of FNAB cytological images of thyroid nodules.