NDLI: A robust and automatic method for human parasite egg recognition in microscopic images

Content Provider	Springer Nature Link
Author	Li, Zhixun Gong, Huiling Zhang, Wei Chen, Lian Tao, Juncai Song, Langui Wu, Zhongdao
Copyright Year	2015
Abstract	With the accelerated movement of population, human parasitoses become an increasingly serious public health’s problem. Currently, detections of parasite eggs through microscopic images are still the golden standard for diagnoses. However, this conventional method relies heavily on the experiences of inspectors, thus giving rise to misdiagnoses and missed diagnoses occasionally. And, as the number of clinical specimens increases rapidly, manual identification seems impractical. Hence, a fully automatic method is in desperate need. In this paper, we propose a robust method to segment and recognize the parasite eggs. Their contours are extracted using phase coherence technology, and the support vector machine (SVM) method based on shape and texture features is employed to classification of parasite eggs. Our novel method was comparable to the traditional method. The overall recognition rate was up to 95 %, and the overall robustness indexes, including si, fnvf, fvpf, tpvf, were 95.7, 4.9, 3.7, 95.1, respectively, suggesting that our method is effective and the robustness is good, which has great potential to become a diagnostic method in the parasitological clinic.
Starting Page	3807
Ending Page	3813
Page Count	7
File Format	PDF
ISSN	09320113
Journal	Parasitology Research
Volume Number	114
Issue Number	10
e-ISSN	14321955
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-07-23
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Automated recognition Image segmentation Human parasite eggs Microscopic image Medical Microbiology Microbiology Immunology
Content Type	Text
Resource Type	Article
Subject	Infectious Diseases Veterinary Parasitology Insect Science

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