Journal of Digital Imaging

, Volume 25, Issue 6, pp 815–818

Quantitative Computed Tomography (QCT) as a Radiology Reporting Tool by Using Optical Character Recognition (OCR) and Macro Program

Authors

  • Young Han Lee
    • Department of Radiology, Research Institute of Radiological Science, Medical Convergence Research Institute, and Severance Biomedical Science InstituteYonsei University College of Medicine
  • Ho-Taek Song
    • Department of Radiology, Research Institute of Radiological Science, Medical Convergence Research Institute, and Severance Biomedical Science InstituteYonsei University College of Medicine
    • Department of Radiology, Research Institute of Radiological Science, Medical Convergence Research Institute, and Severance Biomedical Science InstituteYonsei University College of Medicine
Article

DOI: 10.1007/s10278-012-9464-8

Cite this article as:
Lee, Y.H., Song, H. & Suh, J. J Digit Imaging (2012) 25: 815. doi:10.1007/s10278-012-9464-8

Abstract

The objectives are (1) to introduce a new concept of making a quantitative computed tomography (QCT) reporting system by using optical character recognition (OCR) and macro program and (2) to illustrate the practical usages of the QCT reporting system in radiology reading environment. This reporting system was created as a development tool by using an open-source OCR software and an open-source macro program. The main module was designed for OCR to report QCT images in radiology reading process. The principal processes are as follows: (1) to save a QCT report as a graphic file, (2) to recognize the characters from an image as a text, (3) to extract the T scores from the text, (4) to perform error correction, (5) to reformat the values into QCT radiology reporting template, and (6) to paste the reports into the electronic medical record (EMR) or picture archiving and communicating system (PACS). The accuracy test of OCR was performed on randomly selected QCTs. QCT as a radiology reporting tool successfully acted as OCR of QCT. The diagnosis of normal, osteopenia, or osteoporosis is also determined. Error correction of OCR is done with AutoHotkey-coded module. The results of T scores of femoral neck and lumbar vertebrae had an accuracy of 100 and 95.4 %, respectively. A convenient QCT reporting system could be established by utilizing open-source OCR software and open-source macro program. This method can be easily adapted for other QCT applications and PACS/EMR.

Keywords

Computer in medicinePACSOCRQCTReading room

Copyright information

© Society for Imaging Informatics in Medicine 2012