Cell recognition based on topological sparse coding for microscopy imaging of focused ultrasound treatment

BMC Medical Imaging

Ning Bi²  Changxiu Song³  Yanmei Xue⁴  Jiang Zhu¹  Zhenyou Wang³ 

[1] Department of Ultrasound, Sir Run Shaw Hospital, College of Medicine ZheJiang University, Hangzhou, P.R. China;School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, P. R. China;Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou, P.R. China;The School of Mathematics & Statistics, Nanjing University of Information Science Technology, Nanjing, Jiangsu, P.R. China

关键词: Microscopy imaging;    Focused ultrasound;    Sparse coding;    Topological continuity characteristics;   

Others  :  1233399
DOI  :  10.1186/s12880-015-0087-7

 received in 2014-11-15, accepted in 2015-10-09,  发布年份 2015

【 摘 要 】

Background

Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer; both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment.

Methods

Based on the topological continuity characteristics, and that adjacent points or areas represent similar features, we propose a topological penalized convex objective function of sparse coding, to recognize similar cell phases.

Results

This method introduces new features using a deep learning method of sparse coding with topological continuity characteristics. Large-scale comparison tests demonstrate that the RAW can outperform SIFT GIST and HoG as the input features with this method, achieving higher sensitivity, specificity, F1 score, and accuracy.

Conclusions

Experimental results show that the proposed topological sparse coding technique is valid and effective for extracting new features, and the proposed system was effective for cell recognition of microscopy images of theMDA-MB-231 cell line. This method allows features from sparse coding learning methods to have topological continuity characteristics, and the RAW features are more applicable for the deep learning of the topological sparse coding method than SIFT GIST and HoG.

【 授权许可】

   
2015 Wang et al.

【 图 表 】

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