Edge-shrinking interpolation for medical images

Yuh Hwan Liu, Yung Nien Sun, Chi Wu Mao, Chii Jeng Lin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A new algorithm for interpolating the missing data between two adjacent medical images is presented. Our method is useful for solving the interpolation of any region-represented images of an object to be reconstructed, even when the object is stretched abruptly, branched or hollow, as often occurs in medical images, which cases can not be handled well by existing methods. When this algorithm is applied, the nonoverlapped regions of the same object in the two base images are first extracted and encoded by chamfer distance code on every pixel in these regions. Then, the outer edges of the nonoverlapping regions are shrunk inward simultaneously so that the stretched edges reach the edges of the overlapping regions at the same time. The distance codes in nonoverlapping regions are used to limit the shrinking of these edges in the interpolation process. The proposed method also provides object centralization and enlargement operations to obtain stable and reasonable results in complicated case. The experimental results show that the proposed method is more effective and efficient in resolving general interpolation tasks than the existing methods (S. P. Rayn and J. K. Udupa, IEEE Trans. Med. Imag. 9, 32-42, 1990; G. T. Herman et al., IEEE Comput. Graph. Appl. 12, 69-79, 1992; J. F. Guo et al., Comput. Med. Imag. Graph. 19,267-279, 1995).

Original languageEnglish
Pages (from-to)91-101
Number of pages11
JournalComputerized Medical Imaging and Graphics
Volume21
Issue number2
DOIs
Publication statusPublished - 1997 Mar 1

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Edge-shrinking interpolation for medical images'. Together they form a unique fingerprint.

Cite this