title: Automatic setae segmentation from Chaetoceros microscopic images.
abstract: 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 setae segmentation more accurately and completely.
authors.affiliation: Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, Shandong, 266100, China.
authors.forename: Haiyong
authors.initials: H
authors.lastname: Zheng
authors.forename: Hongmiao
authors.initials: H
authors.lastname: Zhao
authors.forename: Xue
authors.initials: X
authors.lastname: Sun
authors.forename: Huihui
authors.initials: H
authors.lastname: Gao
authors.forename: Guangrong
authors.initials: G
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D000818:Animals; D017377:Diatoms / Q000737:chemistry*; D007090:Image Interpretation, Computer-Assisted; D008853:Microscopy; D058618:Sensilla / Q000737:chemistry*
keywords: GSDAM; biomorphic characteristics; microscopic image segmentation; setae detection