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The project output will be clinically validated by comparing its results with the assessment carried out by medical personnel. The main research goal of the MEDUSA project is a computer aid dia gnostic system that will allow for automated assessment of synovitis activity. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.ĭevelopment of computer systems that can automatically detect and recognize and grade synovitis (joint inflammation) in USG.
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We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. A live analysis functionality also allows users to analyze images captured directly from a microscope.
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Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images.
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Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. The analysis of comet assay output images, however, poses considerable challenges. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. As a result of this scenario, we statistically compared the teaching and learning issues, the user preferences about the tool and the student academic performance. To this end, we conducted an educational experience in robotics subjects with third year students of the computer science and industrial engineering degrees. This lets to fit theoretical and practical works into short development times. As the main contribution, the proposed tool allows to shorten the training time required by students-mainly beginners-without the skills in programming and deep understanding of math hidden behind each image operation. To address this concern, this paper presents an educational tool developed to teach the basic principles of machine vision and image processing through the design of short case studies. These educational approaches are not effective when applying to learners in robotics study programs or without a programming background where time and motivation are different. The educational process is frequently supported by formal lecture approaches assisted by object lessons or lab activities, and project‐based learning methodologies where students engage complex questions, challenges, and problems over a longer period of time. Learning on machine vision and image processing generally require high‐level knowledge on techniques, algorithms and programming skills. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field.
To better serve this purpose, research on the architecture and design of such systems is also important. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception.
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