A Segmentation and Recognition Method of Scallop Shell Growth Pattern

Abstract

The invention provides a method for segmenting and identifying growth patterns of scallop shells. After obtaining CT images of scallop shells, Gaussian kernel matching filter is used to enhance the matching of tubular patterns, and then iterative processing is performed on the enhanced images. Through each iteration The characteristic information of the texture is preserved, and the obtained fine texture is segmented. The present invention uses the CT image of Ezo scallop as the input image, adopts Gaussian kernel matching filter to match and enhance the tubular texture in the image, and performs image segmentation of full variation model image binarization, and then realizes the recognition of intersection points in the image texture network, and obtains A large amount of information on the growth points of scallops is the premise of further accurate calculation of the growth rate of scallops. Compared with the traditional timing measurement method, this method can realize simple and rapid measurement and calculation by computer analysis, and has the advantages of high accuracy and large throughput, which provides a basis for the future development of shellfish breeding work to obtain accurate breeding phenotype information.

Description

Technical field [0001] The invention belongs to the technical field of shellfish genetic breeding, and specifically relates to a method for segmenting and identifying the growth pattern of scallop shells. Background technique [0002] Shells are calcifications secreted by shellfish mantle that protect soft parts. Due to the influence of its own and seasonal environmental factors, shellfish will form growth lines in the process of secreting calcification to form shells. The lines appear as different micro-gulls on the surface, as different densities in the internal structure, and as elemental changes in composition. Studying shell patterns can not only obtain scallop growth information (growth traits) objectively and quickly, but also accurately reveal the specific growth process of the shellfish life history, and more importantly, provide basic data for shellfish breeding. [0003] Patinopectenyessoensis is native to the southern Kuril Islands of Russia, Hokkaido, Japan and north...

AI Technical Summary

Problems solved by technology

These studies only stop at the function fitting of shell data measured regularly during the growth process of scallops, which requires long-term follow-up measurements of scallops, and is also limited by measurement errors and inaccurate manual readings

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