title: A robust and automatic method for human parasite egg recognition in microscopic images.
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.
authors.affiliation: School of Information Engineering, Nanchang University, Nanchang, 330031, China.
authors.forename: Zhixun
authors.initials: Z
authors.lastname: Li
authors.forename: Huiling
authors.initials: H
authors.lastname: Gong
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.forename: Lian
authors.initials: L
authors.lastname: Chen
authors.forename: Juncai
authors.initials: J
authors.lastname: Tao
authors.forename: Langui
authors.initials: L
authors.lastname: Song
authors.forename: Zhongdao
authors.initials: Z
authors.lastname: Wu
mesh_terms: D000818:Animals; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods*; D010063:Ovum / Q000145:classification* / Q000166:cytology*; D010272:Parasitic Diseases / Q000175:diagnosis* / Q000469:parasitology