Open AccessUsing a multiscale image processing method to characterize the periodic growth patterns on scallop shells
Author(s) -
Xing Qiang,
Wei Tengda,
Chen Zhihui,
Wang Yangfan,
Lu Yuan,
Wang Shi,
Zhang Lingling,
Bao Zhenmin
Publication year - 2017
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.2789
Subject(s) - computer science , scallop , shell (structure) , image processing , pattern recognition (psychology) , image (mathematics) , crossover , artificial intelligence , algorithm , biology , ecology , materials science , composite material
The fine periodic growth patterns on shell surfaces have been widely used for studies in the ecology and evolution of scallops. Modern X-ray CT scanners and digital cameras can provide high-resolution image data that contain abundant information such as the shell formation rate, ontogenetic age, and life span of shellfish organisms. We introduced a novel multiscale image processing method based on matched filters with Gaussian kernels and partial differential equation (PDE) multiscale hierarchical decomposition to segment the small tubular and periodic structures in scallop shell images. The periodic patterns of structures (consisting of bifurcation points, crossover points of the rings and ribs, and the connected lines) could be found by our Space-based Depth-First Search (SDFS) algorithm. We created a MATLAB package to implement our method of periodic pattern extraction and pattern matching on the CT and digital scallop images available in this study. The results confirmed the hypothesis that the shell cyclic structure patterns encompass genetically specific information that can be used as an effective invariable biomarker for biological individual recognition. The package is available with a quick-start guide and includes three examples: http://mgb.ouc.edu.cn/novegene/html/code.php.