Automated 3D detection and classification of Giardia lamblia cysts using digital holographic microscopy with partially coherent source

Over the past century, monitoring of Giardia lamblia became a matter of concern for all drinking water suppliers worldwide. Indeed, this parasitic flagellated protozoan is responsible for giardiasis, a widespread diarrhoeal disease (200 million symptomatic individuals) that can lead immunocompromised individuals to death. The major difficulty raised by Giardia lamblia's cyst, its vegetative transmission form, is its ability to survive for long periods in harsh environments, including the chlorine concentrations and treatment duration used traditionally in water disinfection. Currently, there is a need for a reliable, inexpensive, and easy-to-use sensor for the identification and quantification of cysts in the incoming water. For this purpose, we investigated the use of a digital holographic microscope working with partially coherent spatial illumination that reduces the coherent noise. Digital holography allows one to numerically investigate a volume by refocusing the different plane of depth of a hologram. In this paper, we perform an automated 3D analysis that computes the complex amplitude of each hologram, detects all the particles present in the whole volume given by one hologram and refocuses them if there are out of focus using a refocusing criterion based on the integrated complex amplitude modulus and we obtain the (x,y,z) coordinates of each particle. Then the segmentation of the particles is processed and a set of morphological and textures features characteristic to Giardia lamblia cysts is computed in order to classify each particles in the right classes.