Algorithms for Automatic Identification and Analysis of Sri Lankan Anopheles Mosquito Species

Abstract

Microscopic digital image processing algorithms are presented here to automatically detect primary morphological features of Sri Lankan anopheline mosquitoes, as an essential step towards the development of automated identification and analysis of various species of anopheline mosquitoes. Mosquitoes that belong to genus Anopheles spread the causative pathogen of malaria. Perfect and speedy species identification is crucial in any surveillance and control strategies. Currently, morphological taxonomic keys are used to identify various species. Two or more primary morphological characteristics, such as a number of dark spots of wings and pale bands of legs, are used in each step of the hierarchical key. To achieve the automatic detection of the primary morphological features, image processing algorithms performed at three levels. At the pre-processing level, methods work with raw, possibly noisy pixel values, with noise reduction and smoothing. In the mid-level, algorithms are utilized pre-processing results for further means with background removing and spots/bands segmentation. At the final level, techniques try to extract the semantics of spots/bands and counting the spots/bands from the information provided. Thirty samples of anopheline mosquitoes' wings and legs microscopic images were analysed with satisfactory results.