Mathematical algorithm for the automatic recognition of intestinal parasites

Autor: Miguel Quiliano, Robert H. Gilman, Carla Cangalaya, Alicia Alva, Mirko Zimic, Patricia Sheen, Casey Krebs
Rok vydání: 2017
Předmět:
Diphyllobothrium latum
Nematoda
Trichuris
Image Processing
Flatworms
purl.org/pe-repo/ocde/ford#3.03.07 [https]
Helminthiasis
lcsh:Medicine
02 engineering and technology
Pattern Recognition
Automated
Diphyllobothrium/growth & development
Intestinal Parasites
0302 clinical medicine
Diphyllobothrium
Fasciola hepatica/growth & development
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Taeniasis
lcsh:Science
Fascioliasis/diagnosis
Microscopy
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Trichuris/growth & development
Ovum/pathology
Taenia/growth & development
Ellipses
Physical Sciences
Diphyllobothriasis/diagnosis
Engineering and Technology
Diphyllobothriasis
020201 artificial intelligence & image processing
Algorithm
Algorithms
Research Article
Fascioliasis
Trichuriasis
Trichuriasis/diagnosis
030231 tropical medicine
Geometry
Digital Imaging
Biology
Research and Analysis Methods
Trematodes
Sensitivity and Specificity
Helminthiasis/diagnosis
03 medical and health sciences
Helminths
parasitic diseases
medicine
Humans
Animals
Taeniasis/diagnosis
Ovum
Taenia
Curvature
Fasciola Hepatica
lcsh:R
Organisms
Biology and Life Sciences
biology.organism_classification
medicine.disease
Invertebrates
Fasciola
Signal Processing
Trichuris trichiura
Parasitology
lcsh:Q
Mathematics
Zdroj: PLoS ONE, Vol 12, Iss 4, p e0175646 (2017)
PLoS ONE
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0175646
Popis: Parasitic infections are generally diagnosed by professionals trained to recognize the morphological characteristics of the eggs in microscopic images of fecal smears. However, this laboratory diagnosis requires medical specialists which are lacking in many of the areas where these infections are most prevalent. In response to this public health issue, we developed a software based on pattern recognition analysis from microscopi digital images of fecal smears, capable of automatically recognizing and diagnosing common human intestinal parasites. To this end, we selected 229, 124, 217, and 229 objects from microscopic images of fecal smears positive for Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica, respectively. Representative photographs were selected by a parasitologist. We then implemented our algorithm in the open source program SCILAB. The algorithm processes the image by first converting to gray-scale, then applies a fourteen step filtering process, and produces a skeletonized and tri-colored image. The features extracted fall into two general categories: geometric characteristics and brightness descriptions. Individual characteristics were quantified and evaluated with a logistic regression to model their ability to correctly identify each parasite separately. Subsequently, all algorithms were evaluated for false positive cross reactivity with the other parasites studied, excepting Taenia sp. which shares very few morphological characteristics with the others. The principal result showed that our algorithm reached sensitivities between 99.10%-100% and specificities between 98.13%- 98.38% to detect each parasite separately. We did not find any cross-positivity in the algorithms for the three parasites evaluated. In conclusion, the results demonstrated the capacity of our computer algorithm to automatically recognize and diagnose Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica with a high sensitivity and specificity.
Databáze: OpenAIRE
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