A digital image analysis and neural network based system for identification of third-stage parasitic strongyle larvae from domestic animals

A robust and automatic method for human parasite egg recognition in microscopic images

With the accelerated movement of population, human parasitoses become an increasingly serious public health's problem. Currently, detections of parasite eggs through microscopic images are still the golden standard for diagnoses. However, this conventional method relies heavily on the experiences of inspectors, thus giving rise to misdiagnoses and missed diagnoses occasionally. And, as the number of clinical specimens increases rapidly, manual identification seems impractical. Hence, a fully automatic method is in desperate need. In this paper, we propose a robust method to segment and recognize the parasite eggs. Their contours are extracted using phase coherence technology, and the support vector machine (SVM) method based on shape and texture features is employed to classification of parasite eggs. Our novel method was comparable to the traditional method. The overall recognition rate was up to 95%, and the overall robustness indexes, including si, fnvf, fvpf, tpvf, were 95.7, 4.9, 3.7, 95.1, respectively, suggesting that our method is effective and the robustness is good, which has great potential to become a diagnostic method in the parasitological clinic.

A general methodology for the determination of 2D bodies elastic deformation invariants: application to the automatic identification of parasites

A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances so as to quantify mechanoelastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechanoelastic properties of the bodies are stated which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot a deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both of these processes may furnish a body-undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers, and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites, and then, apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into six families, with an accuracy rate of 97.6 percent.