Abstract
.Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist’s experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.
Highlights
Despite recent improvements in our understanding of molecular changes in cancer cells, it remains difficult to diagnose cancer using biologic methods
normal cell (NOR), NET, and regenerative cell (REG), which we classified as negative for intraepithelial lesion or malignancy (NILM)
Pf.[1] includes convex hull (CH) contour complexity (CC) values that represent the complexity of chromatin distribution within the cell nucleus
Summary
Despite recent improvements in our understanding of molecular changes in cancer cells, it remains difficult to diagnose cancer using biologic methods Some biologic methods, such as fluorescent in situ hybridization (FISH) for detecting chromosomal translocation and polymerase chain reaction (PCR) for detecting cell clonality, are sometimes used to assist the cancer diagnosis; cell clonality and chromosomal translocation are not limited features of the cancer cells.[1,2,3,4] Cancer is always diagnosed by pathologists via light microscopic evaluations of histological or cytological samples. Quantitative feature analysis of nuclear atypia can enhance the cytologist’s assessment accuracy
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