Abstract
An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor’s (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohistochemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were used in the IAS’s design. IAS comprised a/image segmentation, for nuclei determination, b/extraction of textural features, by processing of nuclei-images utilizing the Laws and Gabor filters and by calculating textural features from the processed nuclei-images, and c/PNN and SVM classifiers design, for discriminating positively stained nuclei. The proportion of the latter in each case’s images was compared against the physician’s score. Using Spearman’s rank correlation, high correlation was found between the histopathogist’s and IAS’s scores (rho=0.89, p<0.001) and 22/24 cases were correctly characterised, indicating IAS’s reliability in the quantitative evaluation of ER as additional assistance to physician’s assessment.
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Kostopoulos, S. et al. (2007). Assessing Estrogen Receptors’ Status by Texture Analysis of Breast Tissue Specimens and Pattern Recognition Methods. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_28
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DOI: https://doi.org/10.1007/978-3-540-74272-2_28
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