Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Jan;35(1):3-14.
doi: 10.4103/0971-6203.58777.

Automated medical image segmentation techniques

Neeraj Sharma  1 , Lalit M Aggarwal
Affiliations

Automated medical image segmentation techniques

Neeraj Sharma et al. J Med Phys. 2010 Jan.

Abstract

Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

Keywords: Artificial intelligence techniques; computed tomography; magnetic resonance imaging; medical images artifacts; segmentation.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest: None declared.

Figures

Figure 1
Figure 1
Artifacts in MR Imaging
Figure 2
Figure 2
Examples of CT Artifacts: (A) Streak (B) Motion (C) Beam-hardening (D-E) Ring (F) Bloom [4]
Figure 3
Figure 3
Image Histogram (three peaks separated by two minima)
Figure 4a
Figure 4a
Original Abdomen CT Image
Figure 4b
Figure 4b
Segmentation of Abdomen (CT image using threshold technique)
Figure 5
Figure 5
Result of Edge-based Segmentation of Abdomen (CT image)
Figure 6
Figure 6
Segmentation of Abdomen (CT image using region based technique)
Figure 7
Figure 7
Individual Segments of Brain CT Image (A) Original (B-E) Individual segments (F) Segmented image in Pseudo Color
See this image and copyright information in PMC

References

    1. Withey DJ, Koles ZJ. Three generations of medical image segmentation: Methods and available software. Int J Bioelectromag. 2007;9:67–8.
    1. Prince JL, Links JM. Medical imaging signals and system. Pearson Education. 2006.
    1. Macovski A. Medical imaging systems. Prentice-Hall; 1983.
    1. Popilock R, Sandrasagaren K, Harris L, Kaser KA. CT artifact recognition for the nuclear technologist. J Nucl Med Technol. 2008;36:79–81. - PubMed
    1. Li H, Deklerck R, Cuyper BD, Hermanus A, Nyssen E, Cornelis J. Object recognition in brain CT-scans: Knowledge based fusion of data from multiple feature extractors. IEEE T Med Imaging. 1995;14:212–29. - PubMed