Selvan Arul N, Cole Laura M, Spackman Lynne, Naylor Sarah, Wright Chris
Materials and Engineering Research Institute (MERI), Sheffield Hallam University, City Campus, Howard Street, Sheffield S1 1WB, UK.
The Center for Mass Spectrometry Imaging, Biomolecular Science Research Center, Sheffield Hallam University, Sheffield, South Yorkshire, UK.
Methods Mol Biol. 2017;1618:95-123. doi: 10.1007/978-1-4939-7051-3_10.
Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis and subsequent treatment planning. In medical images to visually perceive abnormalities' extent and boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmentation algorithm to segment digital medical image data.Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable dissimilarity between its different regions. The hierarchy represents the continuous merging of similar, spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased.This chapter discusses in detail first the implementation of the HCS process, second the implementation details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and MRI) of a biological sample, third the implementation details of how the process is used as a perception aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpretation aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate.
在不同医学模态的图像中识别异常区域在诊断及后续治疗规划中起着关键作用。在医学图像中，要直观地感知异常的范围和边界需要丰富的经验。因此，手动绘制感兴趣区域（ROI）来勾勒异常边界会受到人类感知局限性的影响，导致观察者间的差异。作为人工绘制ROI的替代方法，有人提出使用基于计算机的分割算法来分割数字医学图像数据。基于层次聚类的分割（HCS）过程是一种通用的无监督分割过程，可用于分割数字图像中的不同区域。HCS过程通过在不同区域之间允许的差异的层次级别上，将图像划分为其组成区域，从而生成一个分割图像的层次结构。随着用于合并的区域之间差异的允许阈值逐渐增加，该层次结构表示相似、空间相邻和/或不相交区域的连续合并。本章首先详细讨论HCS过程的实现，其次讨论HCS过程如何用于呈现生物样本的多模态成像数据（基质辅助激光解吸电离质谱成像和磁共振成像）的实现细节，第三讨论该过程如何用作X射线乳腺造影片读者的感知辅助工具的实现细节，最后讨论它如何用作前列腺多参数磁共振成像（mpMRI）解释辅助工具的实现细节。
