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

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.

Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network

“…ere are various examples of segmentation work [2,7,[12][13][14][15][16][17][18][19]. In one study, the software package er-moMED was used to investigate the ability of thermography to detect multicentric or multifocal breast carcinomas in a preoperative setting [12].…”

“…Important advances in the field have been achieved in [17][18][19]. In one study, the tumor region was found by applying fuzzy c-means for segmentation of the hottest regions in abnormal breasts [17].…”

“…ree image segmentation methods, k-means, fuzzy c-means, and level set, were compared in [18], the level set being a more accurate approach. In [19], a novel lazy snapping method was presented for detecting hot or cold regions in medical thermographic images to segment different diseases in the breast, foot, knees, lower back, and abdomen.…”

Deep Learning Classification for Diabetic Foot Thermograms

“…A hot region in diabetic subjects can be a sign of tissue damage or inflammation. Etehadtavakol et al [38] demonstrated the importance of extracting the hottest/coldest regions from thermographic images using lazy snapping. Lazy snapping is an interactive image algorithm that divides coarse and fine scale processing, accomplishing object condition and detailed adjustment effortlessly.…”

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

“…Acharya et al [28] extracted a co-occurrence matrix and a run length matrix texture features from each infrared image and then fed them into a support vector machine algorithm to discriminate the normal cases from the malignant ones, achieving a mean sensitivity of 85.71%, a specificity of 90.48% and an accuracy of 88.10%. Furthermore, Etehadtavakol et al [29] demonstrated the importance of extracting the hottest/coldest regions from thermographic images and used the Lazy snapping method (an interactive image cutout algorithm) to do so quickly with an easy detailed adjustment.…”