Identification of Cryptosporidium parvum oocysts by an artificial neural network approach.
Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts. A total of 525 digitized images of immunofluorescently labeled oocysts, fluorescent microspheres, and other miscellaneous nonoocyst images were employed in the training of the ANN. The images were cropped to a 36- by 36-pixel image, and the cropped images were placed into two categories, oocyst and nonoocyst images. The images were converted to grayscale and processed into a histogram of gray color pixel intensity. Commercially available software was used to develop and train the ANN. The networks were optimized by varying the number of training images, number of hidden neurons, and a combination of these two parameters. The network performance was then evaluated using a set of 362 unique testing images which the network had never "seen" before. Under optimized conditions, the correct identification of authentic oocyst images ranged from 81 to 97%, and the correct identification of nonoocyst images ranged from 78 to 82%, depending on the type of fluorescent antibody that was employed. The results indicate that the ANN developed were able to generalize the training images and subsequently discern previously unseen oocyst images efficiently and reproducibly. Thus, ANN can be used to reduce human errors associated with the microscopic detection of Cryptosporidium oocysts.
Widmer
Kenneth W
KW
Technical Support Center, U.S. EPA Office of Ground Water and Drinking Water, Cincinnati, Ohio, USA.
Oshima
Kevin H
KH
Pillai
Suresh D
SD
D000465:Algorithms; D000818:Animals; D016785:Cryptosporidium parvum / Q000145:classification* / Q000254:growth & development* / Q000302:isolation & purification; D005455:Fluorescent Antibody Technique; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010274:Parasitology / Q000379:methods; D014867:Water / Q000469:parasitology
D014867:Water