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Count and split images of adherent cells

Author: ZhangYing
Tutor: LuJianFeng
School: Nanjing University of Technology and Engineering
Course: Applied Computer Technology
Keywords: Cell image Image Segmentation Cell count Distance transform Watershed algorithm
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 223
Quote: 2
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Abstract


With the development of computer technology , computer image processing and analysis technology plays an increasingly important role in the clinical diagnosis and treatment . Digital image processing and pattern recognition techniques have been widely used in significant microbial medical field . The use of computers for medical the cells image processing and analysis , can be more accurate than visual analysis and research methods to reduce subjective interference . The computer medical cell image processing and analysis , including image acquisition , image preprocessing, image segmentation, feature extraction, feature analysis , output results . Order to be able to accurate analysis of data on the cell image , the key is to correct the image segmentation, the quality of the segmentation results directly affect the quality of the subsequent image analysis, identification and interpretation , has important significance , which is also on the cell image automatic interpretation and analysis of the difficulties , but also the most critical step . However , despite the large number of domestic and foreign scientists extensive and in-depth study of image segmentation , proposed a lot of fruitful segmentation algorithm , but still do not have a way to test image segmentation results are the best . In this paper, a count adhesion cell image segmentation method based on distance transform and split . This method avoids the problem of over-segmentation of traditional watershed algorithm achieved good segmentation results .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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