publication . Part of book or chapter of book . 2009

Unsupervised clustering using diffusion maps for local shape modelling

Daniel Valdes-Amaro; Abhir Bhalerao;
Open Access English
  • Published: 01 Jan 2009
  • Publisher: Springer Berlin Heidelberg
  • Country: United Kingdom
Abstract
Understanding the biological variability of anatomical objects, is essential for statistical shape analysis and to distinguish between healthy and pathological structures. Statistical Shape Modelling (SSM) can be used to analyse the shapes of sub-structures aiming to describe their variation across individual objects and between groups of them [1]. However, when the shapes exhibit; self-similarity or are intrinsically fractal, such as often encountered in biomedical problems, global shape models result in highly non-linear shape spaces and it can be difficult; to determine a compact set; of modes of variation. In this work, we present, a method for local shape, ...
Subjects
free text keywords: QA, QH301, TA, Point distribution model, Computer science, Statistical shape analysis, Diffusion map, Heat kernel signature, Active shape model, Shape analysis (digital geometry), Machine learning, computer.software_genre, computer, Cluster analysis, Artificial intelligence, business.industry, business, Fractal
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