Unsupervised Segmentation for Inflammation Detection in Histopathology Images
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- Thomas K.A., Sottile M.J., Salafia C.M. (2010) Unsupervised Segmentation for Inflammation Detection in Histopathology Images. In: Elmoataz A., Lezoray O., Nouboud F., Mammass D., Meunier J. (eds) Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol 6134. Springer, Berlin, Heidelberg
Acute inflammation of the placenta is associated with an increased rate of perinatal morbidity and mortality. This paper presents a novel method for analyzing digitized images of hematoxylin and eosin (H&E) stained histology slides to detect and quantify inflammatory polymorphonuclear leukocytes (PMLs). The first step is isolating the tissue by removing the background and red blood cells via color thresholding. Next, an iterative threshold technique is used to differentiate tissue from nuclei. Nuclei are further segmented to distinguish between nuclei which match morphological characteristics of PMLs and those which do not, such as connective tissue fibroblasts as well as chorion and amnion epithelium. Quantification of the remaining nuclei, many of which are likely neutrophils, are associated with amniotic fluid proteomic markers of infection and inflammation, as well as histological grading of neutrophils in amnion, chorion and decidua, and in the umbilical cord.
KeywordsMedical image processing computer aided diagnosis quantitative image analysis histopathology image analysis microscopy images pathological image analysis image processing nuclei morphology
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