Background:

In microbiology laboratories, the identification and counting of microorganisms is a common procedure; and although there is a variety of equipment on the market that possibility to carry out these processes automatically or semi-automatically, it is usually expensive to many laboratories. These are some of the reasons why this arduous and difficult task is still performed in many laboratories by experts in the traditional way, through the observation of samples in microscope, consuming a great time and having variations in the results between experts.

Objective:

The present work aims to propose a new method for counting bacteria and yeasts in digital images, taken under different magnifications, of microbial bioproducts obtained by fermentation.

Methods:

The sensor used to take images of the samples was a digital camera model HDCE-X, with a ½" CMOS sensor, with a resolution of 2592 pixels by 1944 pixels (5 Mp). Two types of magnifications were used: 40x magnification (PL40, 0.65 numerical aperture and 0.17 working distance) and 100x magnification (HI plan 100/1.25 with oil immersion). The proposed method is based on digital image processing technics, using tools as contour detection, morphological operations and statistical analysis, and was developed in Python language using the OpenCV library. The work also presents a comparison with the results obtained using ImageJ software for the same purpose.

Results:

the detection and count of bacteria was achieved with an acceptable accuracy and precision, in both cases above 0.95; not in the case of yeasts whose accuracy and precision was lower, around 0.78 for accuracy and 0.86 for precision. Workflows based on digital image processing techniques are proposed, using tools as contour detection, morphological operations and statistical analysis.

Conclusions:

the method has an acceptable effectiveness for the context and depends on the characteristics presented by the images.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )