Mathematical algorithm for the automatic recognition of intestinal parasites

<div><p>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 <i>Taenia</i> sp., <i>Trichuris trichiura</i>, <i>Diphyllobothrium latum</i>, and <i>Fasciola hepatica</i>, 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 <i>Taenia</i> 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 <i>Taenia</i> sp., <i>Trichuris trichiura</i>, <i>Diphyllobothrium latum</i>, and <i>Fasciola hepatica</i> with a high sensitivity and specificity.</p></div

Flowchart of proposed methodology to automatically recognize parasitic eggs from microscopic photographs.

<p>Flowchart of proposed methodology to automatically recognize parasitic eggs from microscopic photographs.</p

Sensitivity and specificity of each regression model’s ability to recognize parasites in digital images of fecal smears given differing probability cutoff values.

<p>Sensitivity and specificity of each regression model’s ability to recognize parasites in digital images of fecal smears given differing probability cutoff values.</p

Flowchart of the feature extraction for <i>Taenia</i> sp.

<p>Flowchart showing feature extraction for <i>Taenia</i> sp. eggs, resulting in 80 variables for statistical analysis.</p

Summary of classification variables, regression model fit, and odds ratio of each regression’s ability to make an accurate classification.

<p>Summary of classification variables, regression model fit, and odds ratio of each regression’s ability to make an accurate classification.</p

Flowchart of the feature extraction process for <i>Fasciola hepatica</i>, <i>Diphyllobothrium latum</i>, and <i>Trichuris trichiura</i> eggs.

<p>This process results in 92 features for statistical analysis.</p

Summary of the principle variables and the features they represent.

<p>Summary of the principle variables and the features they represent.</p

The processing flow of the image processing for parasite eggs.

<p>The initial input is the original image of the eggs, captured at 40x magnification. Fourteen steps enhance contrast and filter out noise in order to obtain the final images that serve as inputs for the feature extraction process.</p

Prototype 2 system.

<p>(a) <b>Schematic diagram of the prototype 2 system.</b> Magnifier and digital camera prototype with output to digital screen; (b) photograph of prototype 2.</p

Schematic diagram of the prototype 1 system.

<p>Magnifier and digital camera prototype with output to laptop.</p