Automatic Cryptosporidium and Giardia viability detection in treated water

In the automatic detection of Cryptosporidium and Giardia (oo)cysts in water samples, low contrast and noise in the microscopic images can adversely affect the accuracy of the segmentation results. An improved partial differential equation (PDE) filtering that achieves a better trade-off between noise removal and edge preservation is introduced where the compass operator is utilized to attenuate noise while retaining edge information at the cytoplasm wall and around the nuclei of the (oo) cysts. Then the anatomically important information is separated from the unwanted background noise using the Otsu method to improve the detection accuracy. Once the (oo) cysts are located, a simple technique to classify the two types of protozoans using area, roundness metric and eccentricity is implemented. Finally, the number of nuclei in the cytoplasm of each (oo) cyst is counted to check the viability of individual parasite. The proposed system is tested on 40 microscopic images obtained from treated water samples, and it gives excellent detection and viability rates of 97% and 98%, respectively.