Lin Dongyun, Lin Zhiping, Velmurugan Ramraj, Ober Raimund J
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore.
Department of Biomedical Engineering, Texas A&M University, Texas, US.
IEEE Int Symp Circuits Syst Proc. 2017 May;2017. doi: 10.1109/ISCAS.2017.8050242. Epub 2017 Sep 28.
This paper proposes a modified spatially-constrained similarity measure (mSCSM) method for endosomal structure detection and localization under the bag-of-words (BoW) framework. To our best knowledge, the proposed mSCSM is the first method for fully automatic detection and localization of complex subcellular compartments like endosomes. Essentially, a new similarity score and a novel two-stage output control scheme are proposed for localization by extracting discriminative information within a group of query images. Compared with the original SCSM which is formulated for instance localization, the proposed mSCSM can address category based localization problems. The preliminary experimental results show the proposed mSCSM can correctly detect and localize 79.17% of the existing endosomal structures in the microscopic images of human myeloid endothelial cells.
本文提出了一种改进的空间约束相似性度量（mSCSM）方法，用于在词袋（BoW）框架下进行内体结构检测和定位。据我们所知，所提出的mSCSM是第一种用于全自动检测和定位诸如内体等复杂亚细胞区室的方法。本质上，通过在一组查询图像中提取判别信息，提出了一种新的相似性得分和一种新颖的两阶段输出控制方案用于定位。与最初为实例定位而制定的原始SCSM相比，所提出的mSCSM可以解决基于类别的定位问题。初步实验结果表明，所提出的mSCSM能够在人类髓样内皮细胞的显微图像中正确检测和定位79.17%的现有内体结构。
