Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities
2017
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 segmenta-
tion 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 implementa-
tion 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 interpreta-
tion aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate.
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