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Automated image analysis of nuclear atypia in high‐power field histopathological image
Author(s) -
LU CHENG,
JI MENGYAO,
MA ZHEN,
MANDAL MRINAL
Publication year - 2015
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12237
Subject(s) - artificial intelligence , computer science , haematoxylin , pattern recognition (psychology) , eosin , digital image , histogram , digital pathology , image processing , feature (linguistics) , computer vision , atypia , image (mathematics) , pathology , medicine , staining , linguistics , philosophy
Summary Aims We developed a computer‐aided technique to study nuclear atypia classification in high‐power field haematoxylin and eosin stained images. Methods and results An automated technique for nuclear atypia score (NAS) calculation is proposed. The proposed technique uses sophisticated digital image analysis and machine‐learning methods to measure the NAS for haematoxylin and eosin stained images. The proposed technique first segments all nuclei regions. A set of morphology and texture features is extracted from presegmented nuclei regions. The histogram of each feature is then calculated to characterize the statistical information of the nuclei. Finally, a support vector machine classifier is applied to classify a high‐power field image into different nuclear atypia classes. A set of 1188 digital images was analysed in the experiment. We successfully differentiated the high‐power field image with NAS1 versus non‐NAS1, NAS2 versus non‐NAS2 and NAS3 versus non‐NAS3, with area under receiver‐operating characteristic curve of 0.90, 0.86 and 0.87, respectively. In three classes evaluation, the average classification accuracy was 78.79%. We found that texture‐based feature provides best performance for the classification. Conclusion The automated technique is able to quantify statistical features that may be difficult to be measured by human and demonstrates the future potentials of automated image analysis technique in histopathology analysis.