Image segmentation is a significant topic in image refining and automated image analysis with relevance for instance object recognition, diagnostic imaging scanning, mechanized perception, monitoring cameras, satellite imaging, and image compression, and so on. This technology has become an essential component of image assessment as it facilitates the depiction, taxonomy, and conception of the subject matter in the representation. The latest advances in computer vision procedures and the progressive attainability of substantial databases have made it absolutely typical in the computer vision domain. Lately, because of the progression of deep learning techniques, it is observed that a considerable number of tasks are directed at establishing image segmentation strategies operating deep learning models. As an evolving biomedical image refining mechanization, medical image segmentation has computed significant improvements to sustainable health maintenance. Presently it has evolved into a predominant experimentation direction in the domain of computer vision. With the rapid evolution of deep learning, diagnostic image scanning characterized by deep convolutional neural networks has become a research epicentre. This review covers a survey on existing image segmentation approaches into extensive categorization of their algorithms. Additionally, this review outlines the therapeutic and non-therapeutic image databases deployed in the literature for implementing the experimentation. Apart from this, numerous evaluation metrics are discussed for evaluation comparing the results of different segmentation techniques. Further, a detailed discussion on the distinct domains of applications in image segmentation is provided. In conclusion, a discussion on several issues, especially in therapeutic domain and scope in the domain of image segmentation for implementation in the diverse disciplines is provided

中文翻译：

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图像分割综述：理论背景和最新进展


图像分割是图像细化和自动图像分析中的一个重要课题，与目标识别、诊断成像扫描、机械化感知、监控摄像头、卫星成像和图像压缩等相关。这项技术已成为图像评估的重要组成部分，因为它促进了表征中主题的描述、分类和概念。计算机视觉程序的最新进展和大量数据库的不断实现使其成为计算机视觉领域的绝对典型。最近，由于深度学习技术的进步，我们观察到相当多的任务是针对建立操作深度学习模型的图像分割策略。作为一种不断发展的生物医学图像细化机械化，医学图像分割对可持续健康维护做出了重大改进。目前它已发展成为计算机视觉领域的一个主要实验方向。随着深度学习的快速发展，以深度卷积神经网络为特征的诊断图像扫描已成为研究中心。这篇综述涵盖了对现有图像分割方法的调查，并对其算法进行了广泛的分类。此外，这篇综述概述了文献中用于实施实验的治疗性和非治疗性图像数据库。除此之外，还讨论了许多评估指标来评估比较不同分割技术的结果。此外，还详细讨论了图像分割中应用的不同领域。总之，提供了对几个问题的讨论，特别是在治疗领域和在不同学科中实施的图像分割领域的范围