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.

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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.