Microscopic Image Processing for the Analysis of Nosema Disease

In this chapter, the authors tried to develop a tool to automatize and facilitate the detection of Nosema disease. This work develops new technologies in order to solve one of the bottlenecks found on the analysis bee population. The images contain various objects; moreover, this work will be structured on three main steps. The first step is focused on the detection and study of the objects of interest, which are Nosema cells. The second step is to study others' objects in the images: extract characteristics. The last step is to compare the other objects with Nosema. The authors can recognize their object of interest, determining where the edges of an object are, counting similar objects. Finally, the authors have images that contain only their objects of interest. The selection of an appropriate set of features is a fundamental challenge in pattern recognition problems, so the method makes use of segmentation techniques and computer vision. The authors believe that the attainment of this work will facilitate the diary work in many laboratories and provide measures that are more precise for biologists.