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IEEE Int Symp Circuits Syst Proc. 2017 May;2017. doi: 10.1109/ISCAS.2017.8050242. Epub 2017 Sep 28.

Automatic Endosomal Structure Detection And Localization in Fluorescence Microscopic Images.

Author information

1
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore.
2
Department of Biomedical Engineering, Texas A&M University, Texas, US.

Abstract

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.

KEYWORDS:

bag-of-words (BoW); endosomal structures; histogram intersection; spatially-constrained similarity measure (SCSM)

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