Neurocysticercosis (NCC) is considered a major cause of acquired epilepsy in most developing countries. Humans and pigs acquire cysticercosis ingesting T. solium eggs by the fecal-oral route. After ingestion, oncospheres disperse throughout the body producing cysts mainly in the central nervous system and striated muscles. The treatment is focused on antiparasitic, anti-inflammatory, and antiepileptic drugs; however, new drugs are being studied in animal models recently. The aim of this study was to perform histological image analysis of pig brains with NCC after antiparasitic treatment to develop future tools to study brain inflammation since usually the evaluation of fibrosis is obtained manually on microscopy images in a long, inaccurate, poorly reproducible, and tedious process. For this purpose, the slides of pig brains with NCC were stained with Masson's Trichrome, and high quality photographic images were taken. Then, image processing and machine learning were performed to detect the presence and extension of collagen fibers around the cyst as markers of fibrosis. The process includes the use of color normalization and probabilistic classification implemented in Java language as a plugin to the free access program ImageJ. This paper presents a new method to detect cerebral fibrosis, assessing the amount of fibrosis in the images with accuracy above 75% in 12 seconds. A manual editing tool allows us to raise the results above 90% faster and efficiently.
The study of drugs to combat neurodegenerative diseases, such as Alzheimer and Parkinson, is frequently done in animal models such as rats. To evaluate the effectiveness of drugs and administered medication, videos of rats in a swimming pool are recorded and their behavior is analyzed. Although, there are several commercial and free access computer programs that allow recording the movement of the rat, they do not do it in an automatic way, given that the identification of some reference points such as the position and ratio of the pool is done by hand. In addition, it is required to identify the frame when the rat is released. This makes the study of these videos long, tedious and not reproducible. Therefore, in this paper, a new technique for the evaluation of the Morris test is introduced. It automatically detects and localises the pool and the rat notably reducing the time consumed in the evaluation. For the pool identification a segmentation method, based on the projection of the video frames, is done, eliminating the rat, while conserving the shape of the pool. Then, the Hough transformation is used to recognize the position and radius of the pool. The frame when the rat is released is found by using mathematical morphology techniques. The software was developed as a plugin of the free access software imageJ. The results obtained were validated, allowing to verify the quality of the proposed method.
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