Bayesian estimation of geometric morphometric landmarks for simultaneous localization of multiple anatomies in cardiac ct images

Byunghwan Jeon, Sunghee Jung, Hackjoon Shim, Hyuk Jae Chang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.

Original languageEnglish
Article number64
Pages (from-to)1-14
Number of pages14
JournalEntropy
Volume23
Issue number1
DOIs
Publication statusPublished - 2021 Jan

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering

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