Accurate nuclear classification (e.g., grading of renal cell carcinoma (RCC) biopsy images) is important
to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is
proposed to quantitatively analyze RCC data. A novel segmentation methodology is firstly used to delineate
cell nuclei based on minimum description length (MDL) constrained B-spline curve fitting. From the obtained
segmentations, thirteen features are then extracted based on five types of characteristics. These features are used
to classify cell nuclei in biopsy images. Associations among nuclei are computed and represented by graphical
networks to enable further analysis. Finally, a support vector machine (SVM) based decision-graph classifier is
introduced to classify the biopsy images with the purpose of grading. Experimental results on real RCC data show
that our SVM-based decision-graph classifier achieves 95.20% of classification accuracy while the SVM classifiers
achieve 93.33% of classification accuracy.
SONG Jie (宋杰), XIAO Liang* (肖亮), LIAN Zhichao (练智超)
. Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(2)
: 180
-187
.
DOI: 10.1007/s12204-017-1819-3
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