Automatic setae segmentation from Chaetoceros microscopic images
作者: Haiyong ZhengHongmiao ZhaoXue SunHuihui GaoGuangrong Ji
作者单位: 1Department of Electronic Engineering Ocean University of China No. 238 Songling Road Qingdao Shandong 266100 China
刊名: Microscopy Research and Technique, 2014, Vol.77 (9), pp.684-690
来源数据库: Wiley Journal
DOI: 10.1002/jemt.22389
关键词: GSDAMmicroscopic image segmentationbiomorphic characteristicssetae detection
原始语种摘要: ABSTRACT(#br)A novel image processing model Grayscale Surface Direction Angle Model (GSDAM) is presented and the algorithm based on GSDAM is developed to segment setae from Chaetoceros microscopic images. The proposed model combines the setae characteristics of the microscopic images with the spatial analysis of image grayscale surface to detect and segment the direction thin and long setae from the low contrast background as well as noise which may make the commonly used segmentation methods invalid. The experimental results show that our algorithm based on GSDAM outperforms the boundary‐based and region‐based segmentation methods Canny edge detector, iterative threshold selection, Otsu's thresholding, minimum error thresholding, K‐means clustering, and marker‐controlled watershed on the...
全文获取路径: Wiley  (合作)
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