Automatic Detection of Myocardial Contours In Cine-Computed Tomographic Images

K. P. Philip, E. L. Dove, K. B. Chandran, D. D. McPherson, N. L. Gotteiner, M. J. Vonesh, W. Stanford, J. E. Reed, J. A. Rumberger

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Quantitative evaluation of cardiac function from cardiac images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. This method is susceptible to great variability that depends on the experience and knowledge of the particular operator tracing the contours. The particular imaging modality that is used may also add tracing difficulties. Cine-computed tomography (cine-CT) is an imaging modality capable of providing high quality cross-sectional images of the heart. CT images, however, are cluttered, i.e., objects that are not of interest, such as the chest wall, liver, stomach, are also visible in the image. To decrease this variability, investigators have developed computer-assisted or near-automatic techniques for tracing these contours. All of these techniques, however, require some operator intervention to confidently identify myocardial borders. We present a new algorithm that automatically finds the heart within the chest, and then proceeds to outline (detect) the myocardial contours. Information at each tomographic slice is used to estimate the contours at the next tomographic slice, thus allowing the algorithm to work in near-apical cross-sectional images where the myocardial borders are often difficult to identify. The algorithm does not require operator input and can be used in a batch mode to process large quantities of data. An evaluation and correction phase is included to allow an operator to view the results and selectively correct portions of contours. We tested the algorithm by automatically identifying the myocardial borders of 27 cardiac images obtained from three human subjects and quantitatively comparing these automatically determined borders with those traced by an experienced cardiologist.

Original languageEnglish (US)
Pages (from-to)241-253
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume13
Issue number2
DOIs
StatePublished - Jun 1994

Funding

Manuscript received February 21, 1992; revised July 1, 1993. This work was supported in part by a grant from USPHS (NHLBI-HL27035). The associate editor responsible for coordinating the review of this paper and recommending its publication was G. Maguire. K. P. Phillip, E. L. Dove. and K. B. Chandran are with the Department of Biomedical Engineering, University of Iowa, Iowa City, IA USA. D. D. McPherson, N. L. Gotteiner. and M. J. Vonesh are with the Department of Internal Medicine, Northwestern University Medical School, Chicago, IL USA. W. Stanford is with the Department of Radiology, University of Iowa, Iowa City, IA USA. J. E. Reed and J. A. Rumberger are with the Department of Cardiovascular Diseases, Mayo Clinic. Rochester, MN USA. IEEE Log Number 9401 109.

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Automatic Detection of Myocardial Contours In Cine-Computed Tomographic Images'. Together they form a unique fingerprint.

Cite this