Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies

Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background and Objective: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs. Methods: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM. Results: From the mapping results of various organ models with different number of holes, it is observed that Fast mSDM can map the organ models onto their target surfaces efficiently and stably without foldovers while preserving geometrical features. Conclusions: Fast mSDM can map the organ model onto the target surface efficiently and stably, and is applicable to medical applications including Statistical Shape Model.

Original languageEnglish
Pages (from-to)237-250
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Volume157
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Angle- and/or area-preserving mapping
  • Fast modified Self-organizing Deformable Model
  • Model correspondence
  • Organ surface model
  • Statistical Shape Model

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

Fingerprint Dive into the research topics of 'Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies'. Together they form a unique fingerprint.

Cite this