Rapid extraction of the hottest or coldest regions of medical thermographic images.

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...

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Publicado en:Medical & Biological Engineering & Computing Vol. 57; no. 2; pp. 379 - 389
Autores principales: Etehadtavakol, Mahnaz, Emrani, Zahra, Ng, E. Y. K.
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: Springer Nature Feb2019
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Rapid extraction of the hottest or coldest regions of medical thermographic images.
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          Etehadtavakol, Mahnaz
          Emrani, Zahra
          Ng, E. Y. K.
        affil: Medical Image and Signal Processing Research center, Isfahan University of Medical Sciences, 81745-319, Isfahan, Iran
      sug:
        subj:
          Breast Neoplasms Diagnosis
          Algorithms
          Heat
          Thermography Methods
          Middle Age
          Logic
          Breast Pathology
          Image Interpretation, Computer Assisted Methods
          Female
          Information Science Methods
          Human
          Middle Aged: 45-64 years
          Female
      ab: 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.
      pubtype: Academic Journal
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      ougenre: Article
    language: English
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