Abstract
Parasitic infections are generally diagnosed by professionals trained to recognize the morphological characteristics of the eggs in microscopic images of fecal smears. However, this laboratory diagnosis requires medical specialists which are lacking in many of the areas where these infections are most prevalent. In response to this public health issue, we developed a software based on pattern recognition analysis from microscopi digital images of fecal smears, capable of automatically recognizing and diagnosing common human intestinal parasites. To this end, we selected 229, 124, 217, and 229 objects from microscopic images of fecal smears positive for Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica, respectively. Representative photographs were selected by a parasitologist. We then implemented our algorithm in the open source program SCILAB. The algorithm processes the image by first converting to gray-scale, then applies a fourteen step filtering process, and produces a skeletonized and tri-colored image. The features extracted fall into two general categories: geometric characteristics and brightness descriptions. Individual characteristics were quantified and evaluated with a logistic regression to model their ability to correctly identify each parasite separately. Subsequently, all algorithms were evaluated for false positive cross reactivity with the other parasites studied, excepting Taenia sp. which shares very few morphological characteristics with the others. The principal result showed that our algorithm reached sensitivities between 99.10%-100% and specificities between 98.13%- 98.38% to detect each parasite separately. We did not find any cross-positivity in the algorithms for the three parasites evaluated. In conclusion, the results demonstrated the capacity of our computer algorithm to automatically recognize and diagnose Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica with a high sensitivity and specificity.
Highlights
Intestinal parasites are among the most common infectious diseases in humans worldwide, with a higher prevalence in developing countries and economically depressed communities
Given the need to facilitate diagnosis, we propose an algorithm to automatically recognize intestinal parasites without physician input
Automatic segmentation during image processing of the eggs is crucial for the feature extraction and classification stage
Summary
Intestinal parasites are among the most common infectious diseases in humans worldwide, with a higher prevalence in developing countries and economically depressed communities. As such, these infections are considered to be a product of poor living conditions the impact of which is frequently underestimated by public health services. In the last few years the role of these infectious agents, especially on the long term physical and mental development of children, has been increasingly recognized [1, 2] This recognition presents the challenge to search for a sustainable and cost-effective solution to this problem. The methodology used can be divided into three general categories: pre-processing, image processing with feature extraction, and classification [6, 7]
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