The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Medical Image Segmentation Methods, Algorithms, and Applications
Abstract
Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Cited by
Classification of acute lymphoblastic leukemia using deep learning
Rehman, A., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23139 · Cited by 328
Self-citationAn effective skin cancer classification mechanism via medical vision transformer
Aladhadh, S., Alsanea, M., Aloraini, M., Khan, T., Habib, S., & Islam, M. (2022) · Sensors · doi:10.3390/s22114008 · Cited by 113
Microscopic malaria parasitemia diagnosis and grading on benchmark datasets
Rehman, A., Abbas, N., Saba, T., Mehmood, Z., Mahmood, T., & Ahmed, K. T. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23071 · Cited by 57
Self-citationMediSee: Reasoning-based Pixel-level Perception in Medical Images
Tong, Q., Lu, Z., Liu, J., Zheng, Y., & Lu, Z. (2025) · arXiv · doi:10.48550/arxiv.2504.11008 · Cited by 3
Metadata
- Authors
- Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M.
- Venue
- IETE Technical Review
- Publisher
- Informa UK Limited
- Published
- 2014-05-04
- Volume
- 31
- Issue
- 3
- Pages
- 199-213
- ISSN
- 0256-4602 (ISSN-L)
- 0974-5971
- OpenAlex
- W2021707668
- Cited by
- 323
- References
- 2
- FWCI
- 13.95
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Medical Image Segmentation Methods, Algorithms, and Applications. https://app.cochat.ai/papers/10.1080/02564602.2014.906861
- The paper
- Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical Image Segmentation Methods, Algorithms, and Applications. IETE Technical Review. https://doi.org/10.1080/02564602.2014.906861
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
