Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

doi:10.1007/s12204-017-1819-3

上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (2): 180-187.doi: 10.1007/s12204-017-1819-3

Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

SONG Jie (宋杰), XIAO Liang* (肖亮), LIAN Zhichao (练智超)

(School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

出版日期:2017-03-31 发布日期:2017-04-04
通讯作者: XIAO Liang (肖亮) E-mail:xiaoliang@mail.njust.edu.cn

Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

SONG Jie (宋杰), XIAO Liang* (肖亮), LIAN Zhichao (练智超)

(School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Online:2017-03-31 Published:2017-04-04
Contact: XIAO Liang (肖亮) E-mail:xiaoliang@mail.njust.edu.cn

摘要/Abstract

摘要： 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.

关键词: renal cell carcinoma (RCC), nuclei segmentation, nuclear classification, feature selection, associative measurement, grading

Abstract: 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.

Key words: renal cell carcinoma (RCC), nuclei segmentation, nuclear classification, feature selection, associative measurement, grading

中图分类号:

TP 391.4

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]. 上海交通大学学报（英文版）, 2017, 22(2): 180-187.

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.

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Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

Robust Segmentation, Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

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