医学图像分析的入门说明 An introductory illustration of medical image analysis

医学图像分析的入门说明

An introductory illustration of

medical image analysis

内容1。介绍1 2岁。医学图像分析：问题和挑战2 3。所有贡献章节的摘要（挑战和调查结果）5 4。与未来趋势的讨论和总结，8篇参考文献

Contents

1. Introduction 1

2. Medical image analysis: issues and challenges 2

3. Summary of all contributory chapters (challenges and findings) 5

4. Discussion and conclusion with future trends 8

References

1. 计算机视觉和机器智能（称为机器视觉）范式总是在医学图像应用领域得到推广，包括计算机辅助诊断、图像引导放射治疗、地标检测、成像基因组学和大脑连接组学。由于常规临床实践中多模态医学图像数据的大量涌入，医学图像分析及其理解中普遍存在的复杂现实问题是一项艰巨的任务。在医学科学和技术领域，这种先进的计算范式的目标是为人类面临的新问题提供稳健和经济有效的解决方案。医学图像分析包括医学图像增强、分割、分类和目标检测等领域。先进的计算机视觉和计算机智能方法已被应用于图像处理和计算机视觉领域。然而，由于非结构化的医学图像数据，并考虑到在常规临床过程中产生的数据量

1. Introduction

Computer vision and machine intelligence (known as machine vision)paradigms are always promoted in the domain of medical image applications, including computer-assisted diagnosis, image-guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Thecomplex real-life problems prevalent in medical image analysis and itsunderstanding are daunting tasks owing to the massive influx of multimodalmedical image data during routine clinical practice. In the field of medicalscience and technology, the objective of such advanced computationalparadigms is to provide robust and cost-effective solutions for the emergingproblems faced by humanity. Medical image analysis includes the fields ofmedical image enhancement, segmentation, classification, and objectdetection, to name a few. Advanced computer vision and machine intelligence approaches have been employed in the field of image processingand computer vision. However, because of unstructured medical imagedata, and considering the volume of data produced during routine clinical

2. 先进的医学图像分析机器视觉范式

3. 2 Advanced Machine Vision Paradigms for Medical Image Analysis

过程中，元启发式算法的适用性仍有待研究。经典的计算机视觉和机器学习技术往往不能在医学图像分析领域提供一个强有力的解决方案。为了克服这一问题，我们将计算机视觉和机器智能范式的不同组成部分结合在一起；由此产生的混合机器智能技术在设计和性能上更有效率和鲁棒性。与此同时，软计算领域的一些技术也在不断发展，如粗糙集、模糊集和进化计算等。这些先进的计算机视觉和机器智能技术足够有效，可以处理医学图像处理的不同方面。这本书旨在提供一个深入的分析，先进的计算机视觉和机器智能技术使用当代算法在医学图像处理和分析领域。对医学图像切片中普遍存在的形状、轮廓、纹理和先验背景信息的探索是计算机视觉研究领域的关键。它还利用了国际医学协会的体积信息

processes, the applicability of the metaheuristic algorithms remains to beinvestigated. Classical computer vision and machine learning techniques often fall short in offering a strong solution in the field of medical imageanalysis. To overcome this problem, different components of the computervision and machine intelligence paradigms are conjoined; the resultanthybrid machine intelligence techniques are more efficient and robust bydesign and performance. At the same time, some technologies in the field ofsoft computing have evolved, such as rough sets, fuzzy sets, and evolutionary computing. These advanced computer vision and machine intelligence techniques are efficient enough to handle different aspects of medicalimage processing. This book aims to provide an in-depth analysis ofadvanced computer vision and machine intelligence techniques usingcontemporary algorithms in the field of medical image processing andanalysis. The exploration of shape, contour, texture and prior contextualinformation prevalent in medical image slices is the key in the field ofcomputer vision research. It also exploits the volumetric information ofmedical imag sequences by processing voxels (three-dimensional [3D] and4D information) for accurate and efficient segmentation. There are plethoraof machine learning and computer vision paradigms (image reconstruction,image classification, image segmentation, tracking, etc.) that enable expertsto analyze disease better using the required relevant information of medicalimages with minimal human intervention.

2. 医学形象分析在人类社会中扮演着重要的角色。医学分析通常会分析各种医疗问题，并通过考虑几种成像方式来纠正它们。除此之外，各种数字图像分析方法在多个场合被用来从这些分析中获得良好的结果。医学成像系统可以产生大量的信息和大量的图像。然而，这些信息基本上是隐藏在数据中的。为了处理这种情况，需要图像分析技术来提取信息，这需要很容易地做出正确的医疗决策。医学图像分析已经得到了非常充分的研究。因此，研究人员已经成功地开发了几种算法和方法来解决医学图像问题。图像分析的成功也在准确性、可信度和速度[1,2]方面被商业应用。

3. 2. Medical image analysis: issues and challenges Medical image analysis has a significant role in human society. Medical analysis generally analyzes various medical problems and rectifies them by considering several imaging modalities. Apart from this, various digital image analysis methods are used on several occasions to achieve a good outcome from these analyses. Medical imaging systems may generate a gigantic amount of images with a huge quantity of information. However, the information is essentially concealed in the data. To deal with this situation, image analyses techniques are required to extract the information, which needs to be easily available for make correct medical decisions. Medical image analysis has been exceedingly well-studied. As a consequence, researchers have successfully developed several algorithms and approaches to address medical image issues. The success of image analysis has also been commercially employed on several occasions with regard to accuracy, trustworthiness, and speed [1,2].