Abstract
Ultrasound B-mode images of thyroid gland were previously analyzed to distinguish normal tissue from inflamed tissue due to Hashimoto’s Lymphocytic Thyroiditis. This is a two-class recognition problem. Sensitivity and specificity of 100% was reported using Bayesian classifier with selected texture features. These results were obtained on 99 subjects at a fixed setting of one specific sonograph, for a given manual thyroid gland segmentation and sonographic scan orientation (longitudinal, transversal). To evaluate the reproducibility of the method, sensitivity analysis is the topic of this paper. A general method for determining feature sensitivity to variables influencing the scanning process is proposed. Jensen Shannon distances between modified and unmodified inter- and intra-class feature probability distributions capture the changes induced by the variables. Among selected features, the least sensitive one is found. The proposed sensitivity evaluation method can be used in other problems with complex and non-linear dependencies on variables that cannot be controlled.
This work has been supported by the Grant Agency of the Czech Academy of Sciences under project 1ET101050403 and by the Czech Ministry of Health under project NO/7742-3.
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Švec, M., Šára, R., Smutek, D. (2005). On Reproducibility of Ultrasound Image Classification. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_54
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DOI: https://doi.org/10.1007/11492542_54
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