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Christian Pavillar Edulan posted an update in the group
MT 30 – IJ (LEC) 4 years, 3 months ago Heart disease kills more people than any other cause of mortality in the world. Therefore, in this study, the development of new diagnostic and therapeutic methods in this sector has become a major focus of scientific investigation. Medical education and practice might benefit from new techniques to tissue image processing in particular. Morphological information may be used to identify and classify basic tissues in an automated manner.
Automatic recognition of fundamental tissues on histology images of the human cardiovascular system – PubMedCardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an app …-
Currently, basic tissue identification is done manually by histologists or other medical professionals, and there are no automated methods. Automation of basic tissue identification may be highly beneficial to histologists, biologists, pathologists, and other relevant fields. For on-campus students and remote students, automated tissue identification might expand the number of instances that can be analysed, increasing self-learning. So, better professionals without a huge social or economic investment. These systems might also automate the sorting and labeling of enormous collections of digital pictures captured by a microscope or supplied by hospitals or histologists. Automatic labeling eliminates subjectivity, time, difficulty, and impracticality associated with human annotation. They proposed to automatically recognize basic cardiovascular tissues in this study. -
Also, some histopathological image processing techniques recognize basic tissues indirectly. Some examples include texture descriptors and segmentation to gland identification utilizing color-threshold, cell location, and contour. The indirect findings may not be correct since the authors want to extract additional information from processed photos, such as glands or malignant tissue.
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The cell nucleus is a critical component in identifying biological structures, hence computer-aided recognition technology pays close attention to it. Previously, computer-aided cell nuclei identification has used approaches such as clustering, picture characteristics, and machine learning. These investigations employed diverse stains and picture types, and their aims were nuclei segmentation – area, outlines, and counts – which required manually configuring parameters.
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The strong border between loose connective and muscular tissues, for example, and poor clarity in picture regions like edges may make computer-aided detection of essential tissues difficult. The significance of essential tissues and their complexity might make automated identification difficult.
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Their proposal includes early breakthroughs in component identification and categorization. These procedures use image processing and tissue morphology data. They focused on segmenting and classifying cells and fibres to identify epithelial, loose connective, and muscular tissues.
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This research proposes a technique for automated identification of basic tissues using morphological information. Muscle, loose connective and epithelial tissues are recognized. A more thorough evaluation is offered in this study, increasing the number of photos and adding additional measures in the results section to compare their early achievements.
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