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BioMed Research International
Volume 2017 (2017), Article ID 3764576, 12 pages
Research Article

Shape and Boundary Similarity Features for Accurate HCC Image Recognition

Software College, Northeastern University, Shenyang 110819, China

Correspondence should be addressed to Huiyan Jiang

Received 26 July 2017; Accepted 28 September 2017; Published 7 November 2017

Academic Editor: Marlene Benchimol

Copyright © 2017 Xiaoyu Duan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nucleus morphology is of great importance in conventional cancer pathological diagnosis, which could provide information difference between normal and abnormal nuclei visually. Therefore, this paper proposes two novel kinds of features for normal and hepatocellular carcinoma (HCC) nucleus recognition, including shape and boundary similarity. First, each individual nucleus patch with the fixed size is obtained using center-proliferation segmentation (CPS) method. Then, nucleus shape library is constructed based on manual selection by pathologists, which is utilized to measure nucleus shape similarity via Dice, Jaccard, precision, and recall coefficients. Meanwhile, boundary similarity is evaluated through triangles composed of some boundary feature points for each nucleus. Finally, the conventional random forest (RF) is used to train and test the classification model for HCC nucleus recognition. Extensive cross-validation tests could facilitate the selection of the optimal feature set and the experiment comparison results demonstrate that our proposed morphological features are more beneficial for classification compared with other traditional characteristics.