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Detection of tubule boundaries based on circular shortest path and polar‐transformation of arbitrary shapes
Author(s) -
SU R.,
ZHANG C.,
PHAM T.D.,
DAVEY R.,
BISCHOF L.,
VALLOTTON P.,
LOVELL D.,
HOPE S.,
SCHMOELZL S.,
SUN C.
Publication year - 2016
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12421
Subject(s) - transformation (genetics) , weighting , polar , polar coordinate system , boundary (topology) , tubule , computer science , shortest path problem , path (computing) , tracing , noise (video) , image (mathematics) , artificial intelligence , pattern recognition (psychology) , algorithm , computer vision , mathematics , physics , chemistry , biology , geometry , theoretical computer science , endocrinology , mathematical analysis , graph , acoustics , operating system , biochemistry , programming language , kidney , astronomy , gene
Summary In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton‐based polar‐transformation, boundary weighting of polar‐transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.