Automatic colorization of non-enhanced brain CT images for clinical diagnosis
Researcher Bulletin of Medical Sciences,
Vol. 27 No. 1 (2022),
20 November 2022
,
Page e13
Abstract
Background: The frequent use of brain computed tomography (CT) scans in emergency settings necessitates accurate reporting of CT results as quickly as possible. Conventional CT scans produce grayscale images, requiring window width and center level changes, resulting in a need for time-consuming interpretation by experienced radiologists. This study aimed to design a novel software application for automatic smart colorization of conventional brain CT images and to evaluate the diagnostic accuracy, visual quality, ease of diagnosis, and reporting time for color CT images compared to conventional grayscale CT images.
Materials and Methods: First, we designed an application that converted non-enhanced grayscale brain CT images into color images according to the Hounsfield unit value of different tissues (e.g., brain, fat, bone, fluid, air) with minimal noise so that all brain tissues could be evaluated using one window level. This process took less than one second, without the need for high-end systems. Next, 75 printed images (25 unprocessed grayscale CT, 25 processed color CT, and 25 magnetic resonance imaging [MRI]) from 25 patients with hemorrhagic or ischemic stroke were read by two experienced radiologists. The radiologists scored the CT images from each patient (unprocessed grayscale and processed color) on a ten-point scale for visual quality and ease of diagnosis compared to the MRI image.
Results: The mean visual quality score was 18% higher and the mean ease of diagnosis score was 23% higher for colorized images than for grayscale images (both P < 0.001). Statistically, there were no significant differences in the diagnostic accuracy or reporting time between color and grayscale images.
Conclusion: This is the first study to report automatic smart colorization of non-enhanced brain CT images, producing high-quality colorized images with better visual quality and ease of diagnosis compared to grayscale CT. This low-cost solution can be widely applied in clinical settings, regardless of minimal facility or resource availability.
- Acute stroke; Automatic data processing; Image enhancement; Software design
How to Cite
References
2. Mullins ME, Schaefer PW, Sorensen AG, Halpern EF, Ay H, He J, et al. CT and Conventional and Diffusion-weighted MR Imaging in Acute Stroke: Study in 691 Patients at Presentation to the Emergency Department. Radiology. 2002;224(2):353-60.
3. Lev MH, Farkas J, Gemmete JJ, Hossain ST, Hunter GJ, Koroshetz WJ, et al. Acute Stroke: Improved Nonenhanced CT Detection—Benefits of Soft-Copy Interpretation by Using Variable Window Width and Center Level Settings. Radiology. 1999;213(1):150-5.
4. Pomerantz SM, White CS, Krebs TL, Daly B, Sukumar SA, Hooper F, et al. Liver and bone window settings for soft-copy interpretation of chest and abdominal CT. AJR Am J Roentgenol. 2000;174(2):311-4.
5. Reiner BI, Siegel EL, Hooper FJ. Accuracy of Interpretation of CT Scans: Comparing PACS Monitor Displays and Hard-Copy Images. American Journal of Roentgenology. 2002;179(6):1407-10.
6. Seletchi ED, Duliu O. Image Processing and Data Analysis in Computed Tomography. Romanian Journal of Physics. 2007;72:764-74.
7. Lagodzinski P, Smolka B, editors. Colorization of Medical Images. APSIPA Annual Summit and Conference; 2009 Oct; Sappora, Japan.
8. Shah AA, Gandhi M, Shah KM. Medical Image Colorization using Optimization Technique. International Journal of Scientific and Research Publications. 2013;3(3).
9. Muehlematter UJ, Caviezel C, Martini K, Messerli M, Vokinger KN, Wetzler IR, et al. Applicability of color-coded computed tomography images in lung volume reduction surgery planning. J Thorac Dis. 2019;11(3):766-76.
10. Bier G, Bongers MN, Ditt H, Bender B, Ernemann U, Horger M. Accuracy of Non-Enhanced CT in Detecting Early Ischemic Edema Using Frequency Selective Non-Linear Blending. PLoS One. 2016;11(1):e0147378.
11. Matsuda S, Yoshimura H, Yoshida H, Ryoke T, Yoshida T, Aikawa N, et al. Usefulness of Computed Tomography Image Processing by OsiriX Software in Detecting Wooden and Bamboo Foreign Bodies. BioMed Research International. 2017;2017:3104018.
12. Merry RJE. Wavelet theory and applications: a literature study. Eindhoven: Technische Universiteit Eindhoven; 2005.
13. Donoho DL. De-noising by soft-thresholding. IEEE transactions on information theory. 1995;41(3):613-27.
14. Rai RK, Sontakke TR. Implementation of image denoising using thresholding techniques. International Journal of Computer Technology and Electronics Engineering (IJCTEE). 2011;1(2):6-10.
15. Nida N, Sharif M, Ghani U, Yasmin M, Fernandes S. A framework for automatic colorization of medical imaging. IIOABJ. 2016;7:202–9.
16. Khan MUG, Gotoh Y, Nida N, editors. Medical Image Colorization for Better Visualization and Segmentation. Medical Image Understanding and Analysis; 2017 2017//; Cham: Springer International Publishing.
17. Selvapriya B, Raghu B. Colorization using desired color for medical images. International Journal of Recent Technology and Engineering(TM). 2019;7(6S3):124-32.
- Abstract Viewed: 148 times