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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial

M Alper Selver1, Cüneyt Güzeliş

  • 1Department of Electrical and Electronics Engineering, Dokuz Eylül University, Izmir, Turkey. alper.selver@deu.edu.tr

IEEE Transactions on Visualization and Computer Graphics
|March 14, 2009
PubMed
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This study introduces a semi-automatic method for generating Transfer Functions (TF) for medical image visualization. The approach uses a novel Self Generating Hierarchical Radial Basis Function Network to improve rendering quality and reduce optimization time.

Area of Science:

  • Medical Imaging
  • Computer Graphics
  • Artificial Intelligence

Background:

  • Transfer Functions (TF) are crucial for interactive visualization in medical imaging.
  • Accurate TF generation is challenging due to the trade-off between search space and user expectations.

Purpose of the Study:

  • To introduce a semi-automatic method for the initial generation of Transfer Functions (TF).
  • To address the time-consuming nature of manual TF optimization in medical image visualization.

Main Methods:

  • A Self Generating Hierarchical Radial Basis Function Network is employed.
  • A novel Volume Histogram Stack (VHS) domain is introduced by aligning image slice histograms.
  • The method identifies suppressed lobes and overlapping regions in the VHS.

Related Experiment Videos

Last Updated: Jun 24, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Main Results:

  • Enhanced rendering quality in medical images was observed.
  • Reduced optimization time for Transfer Functions was achieved.
  • Effective application on various CT and MR abdominal datasets.

Conclusions:

  • The proposed semi-automatic TF generation method improves visualization quality.
  • The Self Generating Hierarchical Radial Basis Function Network and VHS domain offer an efficient approach.
  • This method aids physicians in interactive data exploration and reduces manual effort.