LFDetection – Lynphatic Filiariasis Detection


Lymphatic filariasis (commonly known as elephantiasis) is a parasitic disease that occurs when filarial parasites are transmitted from mosquitoes to humans. When a mosquito with infective stage larvae bites a person, the parasites are deposited on the person’s skin, entering the body. The larvae then migrates to the lymphatic vessels where it develops into adult worms in the human lymphatic system. Infection is usually acquired in childhood, but the painful and profoundly disfiguring visible manifestations of the disease occur later in life. Whereas acute episodes of the disease cause temporary disability, lymphatic filariasis leads to permanent disability.

Currently, more than 1.4 billion people in 73 countries are at risk. Approximately 65% of those infected live in the South-East Asia Region, 30% in the African Region, and the remainder in other tropical areas. Lymphatic filariasis afflicts over 25 million men with genital disease and over 15 million people with lymphoedema.

The main goal of this project is to explore image processing and analysis techniques for the recognition and counting of filarial worms in a giemsa stained blood smear. This implicates to detect and distinguish the worms of the remaining blood elements, as well as distinguish the two most common filarial worms (W.Bancrofti and B.Malayi). The images can be acquired with a generic microscope using the ‘Skylight’ smartphone-to-microscope adapter (requiring a magnification of 40x3), but a simpler smartphone magnification gadget might serve the purpose. This detection might open the path to the revealing of other blood worms and inclusively for the diagnosis of other diseases like, for instance, Loa Loa. It is expected that this analysis can be performed either in a central server as in a mobile platform.


Main Outcomes:

Develop image processing and analysis techniques for the recognition, identification and counting of two kinds of filarial worms (W.Bancrofti and B.Malayi) in the blood stream. It is expected that the analysis can be performed either in a central server as in a mobile platform.



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