Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139237
Title: Rapid extraction of the hottest or coldest regions of medical thermographic images
Authors: Etehadtavakol, Mahnaz
Emrani, Zahra
Ng, Eddie Yin Kwee
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Etehadtavakol, M., Emrani, Z., & Ng, E. Y. K. (2019). Rapid extraction of the hottest or coldest regions of medical thermographic images. Medical & biological engineering & computing, 57(2), 379–388. doi:10.1007/s11517-018-1876-2
Journal: Medical & biological engineering & computing
Abstract: Early detection of breast tumors, feet pre-ulcers diagnosing in diabetic patients, and identifying the location of pain in patients are essential to physicians. Hot or cold regions in medical thermographic images have potential to be suspicious. Hence extracting the hottest or coldest regions in the body thermographic images is an important task. Lazy snapping is an interactive image cutout algorithm that can be applied to extract the hottest or coldest regions in the body thermographic images quickly with easy detailed adjustment. The most important advantage of this technique is that it can provide the results for physicians in real time readily. In other words, it is a good interactive image segmentation algorithm since it has two basic characteristics: (1) the algorithm produces intuitive segmentation that reflects the user intent with given a certain user input and (2) the algorithm is efficient enough to provide instant visual feedback. Comparing to other methods used by the authors for segmentation of breast thermograms such as K-means, fuzzy c-means, level set, and mean shift algorithms, lazy snapping was more user-friendly and could provide instant visual feedback. In this study, twelve test cases were presented and by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for these twelve cases. It was concluded that lazy snapping was much faster than other methods applied by the authors such as K-means, fuzzy c-means, level set, and mean shift algorithms for segmentation. Graphical abstract Time taken to implement lazy snapping algorithm to extract suspicious regions in different presented thermograms (in seconds). In this study, ten test cases are presented that by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for the ten cases. It concludes lazy snapping is much faster than other methods applied by the authors.
URI: https://hdl.handle.net/10356/139237
ISSN: 0140-0118
DOI: 10.1007/s11517-018-1876-2
Rights: © 2018 International Federation for Medical and Biological Engineering. All rights reserved. This paper was published by Springer in Medical & biological engineering & computing and is made available with permission of International Federation for Medical and Biological Engineering.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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