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17 October 2017 Feature analysis of cell nuclear chromatin distribution in support of cervical cytology
Hideki Komagata, Takaya Ichimura, Yasuka Matsuta, Masahiro Ishikawa, Kazuma Shinoda, Naoki Kobayashi, Atsushi Sasaki M.D.
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Journal of Medical Imaging, Vol. 4, Issue 4, 047501 (October 2017). https://doi.org/10.1117/1.JMI.4.4.047501
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Hideki Komagata, Takaya Ichimura, Yasuka Matsuta, Masahiro Ishikawa, Kazuma Shinoda, Naoki Kobayashi, and Atsushi Sasaki M.D. "Feature analysis of cell nuclear chromatin distribution in support of cervical cytology," Journal of Medical Imaging 4(4), 047501 (17 October 2017). https://doi.org/10.1117/1.JMI.4.4.047501
Received: 2 May 2017; Accepted: 26 September 2017; Published: 17 October 2017
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Cited by 8 scholarly publications.
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KEYWORDS
Californium
Cell biology
Machine learning
Diagnostics
Cancer
Feature extraction
Feature selection
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