Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study
© The Author(s) 2017
Received: 25 May 2017
Accepted: 25 September 2017
Published: 3 October 2017
Nowadays, the whole world is being concerned with a major health problem, which is diabetes. A very common symptom of diabetes is the diabetic foot ulcer (DFU). The early detection of such foot complications can protect diabetic patients from any dangerous stages that develop later and may require foot amputation. This work aims at building a mobile thermal imaging system that can be used as an indicator for possible developing ulcers.
The proposed system consists of a thermal camera connected to a Samsung smart phone, which is used to acquire thermal images. This thermal imaging system has a simulated temperature gradient of more than 2.2 °C, which represents the temperature difference (in the literature) than can indicate a possible development of ulcers. The acquired images are processed and segmented using basic image processing techniques. The analysis and interpretation is conducted using two techniques: Otsu thresholding technique and Point-to-Point mean difference technique.
The proposed system was implemented under MATLAB Mobile platform and thermal images were analyzed and interpreted. Four testing images (feet images) were used to test this procedure; one image with any temperature variation to the feet, and three images with skin temperature increased to more than 2.2 °C introduced at different locations. With the two techniques applied during the analysis and interpretation stage, the system was successful in identifying the location of the temperature increase.
This work successfully implemented a mobile thermal imaging system that includes an automated method to identify possible ulcers in diabetic patients. This may give diabetic patients the ability for a frequent self-check of possible ulcers. Although this work was implemented in simulated conditions, it provides the necessary feasibility to be further developed and tested in a clinical environment.
KeywordsUlcer detection Mobile thermal camera Thermal imaging Otsu thresholding Image registration
Diabetes Mellitus (DM) is a metabolic chronic disease that is associated with abnormal glucose levels in the blood. There are two causes of Diabetes Mellitus, the first one is the abnormal production of insulin by the pancreas (Type I), while the second cause is related to inadequate cells action to insulin (Type II). Both types of Diabetes Mellitus can pose a serious threat to patients’ health concerning the cardiovascular system, kidneys, and extremities such as the feet . According to the World Health Organization (WHO), this disease has been dramatically spreading and growing worldwide with an estimation of 422 million adults who live with diabetes in 2014, compared to 108 million adults in 1980 . One of the most dangerous symptoms of this disease is foot complications. Around 15 to 25% of diabetic patients are going to suffer from foot complications at a later stage of the disease . These complications occur as a consequence of infection, peripheral ischemia, and ulceration in the foot [4, 5]. Foot ulcer happens mainly because diabetes introduces peripheral neuropathy, which affects the ability of the foot to feel and sense. That being considered, any injury in the foot can go unnoticed [6, 7]. Pre-signs for such complications include fissures, blisters, abundant callus formation, redness, and increased temperature regions . A physician can check and analyze these physical features in order to diagnose the case. Foot complications can severely develop and result in limb amputation within the foot or even death if left untreated (diabetic foot) . In patients with Diabetes Mellitus disease, approximately 85% of all lower extremity amputations are preceded by foot ulcer .
Diabetic foot ulcer can be avoided or delayed if adequately treated at an early stage. Currently, the assessment of such foot complications is done frequently by clinicians through analyzing blood circulations, plantar foot pressure, and foot neuropathy [9, 10]. Moreover, specialist clinicians usually assess lower extremity vascular status using Doppler ultrasound. This allows the possibility of getting accurate analysis regarding the current situation of foot ulcers and its risks . However, patients are forced to go for frequent visits to doctors for diabetic foot assessment, which is considered intrusive and costly. In addition, self-assessment is considered difficult because it depends on the knowledge of patients with this disease, and on the usage of medical equipment. The treatments for such complications are commonly associated with therapeutic footwear, foot education, and normal foot care . For example, a modified walking apparatus is used to provide consistent pressure relief at the diabetic patient’s foot. Thus, the prevention of more developed stages of current foot complications situation can be maintained and even healed .
The occurrence of diabetic foot complications is often related to the plantar region temperature distribution. Increased temperature may be present in the foot a week before a neuropathic ulcer appears . Researchers often use technologies such as the liquid crystal thermography (LCT) and infrared (IR) thermography to demonstrate the temperature variations . LCT is a color representation proportional to the temperature of the in-contact foot surface with the thermochromic liquid crystal . However, the infrared (IR) thermal imaging is much preferable because of being a non-invasive technology that acquires thermal images based on the heat emitted from the body. Infrared radiations are waves from the electromagnetic spectrum with a range of 760 nm to 1 mm . This technology has made it possible to measure any increased temperature that occurs in some regions within the foot. A 1 °C temperature increase within the foot over the normal foot mean temperature requires an accurate assessment in order to decide whether it is a normal increase or an occurrence of foot ulcers [12, 18, 19]. Moreover, temperature differences of more than 2.2 °C between a region on one foot and the same region on the contra-lateral foot are considered Hyperthermia [14, 19]. Monitoring such differences through thermal images proved to be an efficient way of detecting diabetic foot ulceration.
The aim of this work is to build an ulcer detection/indication system based on a mobile thermal camera and a mobile application. The proposed system would serve as a self-monitoring tool with a mobile app giving diabetic patients the ability to self-check their extremities for any possible ulcer, without the need for frequent visits to the diabetic clinic. The proposed system was implemented using a mobile application where thermal images were acquired, processed, and analyzed for any possible ulceration. Two image-processing techniques were deployed to detect possible ulcers automatically: the Otsu thresholding techniques and the point-to-point difference techniques. Both techniques were tested on thermal images. The implementation of the image processing algorithms was done using MATLAB mobile (Mathworks, Inc.). It was also complied into Java, and a mobile application was built for this purpose.
The proposed system consists of a hardware part, which is mainly a mobile thermal image acquisition camera and a smart phone, along with image processing and analysis software. The entire software was run on MATLAB and was later implemented on a mobile phone through MATLAB Mobile Android application. After that, the entire software was compiled into Java code to build a complete and integrated application with user interface.
Thermal image acquisition system
Thermal imaging system
Image acquisition and measurement procedure
Image analysis and interpretation
The technique of Histogram shape segmentation resulted in a binary image of the segmented foreground, which were the plantar feet. However, some images are not easy to segment, especially if there are some parts within the image where the temperature of the background is close to the foreground temperature. As a result, dark objects are marked as bright objects and vise-versa. This affects any further image processing analysis of the plantar feet image. Therefore, image-smoothing techniques were performed to avoid such errors.
At the beginning, borders clearing technique was used to remove any objects that are connected to the border of the image, or even separated in different places within the image. This was done by suppressing any light structures and removing them from the surrounding border of the image. Then, segments’ smoothing was performed to erode the resulting image with a diamond-structuring element. The element used had a single pixel distance from the origin of its structure to the points of the diamond. This prepares the feet segment to be smoothed at the edges and ensures that no unconnected objects are taking place in the image. Finally, the binary segment created might include some interior gaps, therefore, these gaps were filled with hole-filling objects . The plantar feet segments were ready to be analyzed for any occurrence of diabetic feet abnormalities.
Analysis and observation
After segmenting the plantar feet image, further processing was performed to identify any possible ulcers or occurrence of hyperthermia (a 2.2 °C difference) [12, 18, 19]. Two techniques were used for this purpose, Otsu thresholding technique (discussed in the previous section) and point-to-point difference technique.
Otsu thresholding technique
Point-to-point mean difference
The processed image included both feet; therefore, it was automatically divided into two equal parts; one for the left foot and one for the right foot. As previously mentioned in the acquisition procedure, users are advised to maintain that their feet are in the center of the camera field-of-view for accurate cutting and analysis results. The left foot segment was chosen as the reference foot (the right foot can also be chosen). The deployed technique requires both feet to be aligned together. Hence, two steps were performed; the first one was flipping the right foot to make both feet look identical and the second step was image registration to align both feet together. The adopted image registration technique was the intensity-based registration . Image registration allows both images (left foot and right foot) to be aligned in a way that makes them spatially corresponding to each other .
FLIR ONE complete specifications
Lepton and standard VGA
Scene temperature range
−20 to 120 °C
0 to 35 °C
160 × 120
Mean temperature values (µn, µh, and µd) for test images 1 to 8
Image 1 (°C)
Image 2 (°C)
Image 3 (°C)
Image 4 (°C)
Image 5 (°C)
Image 6 (°C)
Image 7 (°C)
Image 8 (°C)
Feet mean temperature (µn)
Suspected region mean temperature (µh)
Mean difference (µd)
1.8 (No Ulcer)
The main objective of this research was to build a thermal imaging system based on smart phone. The proposed system incorporated the hardware as well as the necessary image processing and interpretation software techniques. According to the obtained results, the proposed system has successfully identified regions with hyperthermia with temperature gradient greater than 2.2 °C which is considered as the value that can be used to identify possible ulcers [14, 19]. The proposed system can also be used to identify temperatures less than this value, but they are not considered as possible ulcers according to the literature. The testing procedure was implemented on four images; one with no temperature gradient introduced and the other three images with thermal gradient at three different locations. The two techniques deployed for image interpretation and analysis were successful in identifying regions with thermal gradient representing possible ulcers. The histogram thresholding techniques used the statistical t-test to verify if the difference between the background and the potential region were statistically different with the mean values listed in Table 2.
Complete observations for test images 1 to 8
Image 1 (°C)
Image 2 (°C)
Image 3 (°C)
Image 4 (°C)
Image 5 (°C)
Image 6 (°C)
Image 8 (°C)
Actual camera measured difference
Otsu thresholding mean difference
Point-to-point mean difference
The entire system was implemented on a Smartphone under MATLAB mobile with processing on a cloud MATLAB server. The use of MATLAB mobile provides the flexibility to do further processing, store the data on a cloud account, and build the necessary interface.
Conclusion and future work
The proposed system provides a framework to build a complete mobile system that can help diabetic patients self-check their feet for any possible ulcers. The system provides only an indicative tool, not a diagnostic tool, as the final diagnosis should be done by the physician (the gold standard). The future work requires upgrading the system with an advanced thermal camera with higher image quality that can be connected to a mobile in order to perform the necessary processing. Further testing and validation of the system should be performed under clinical environment, which was not possible at this stage due to the strict regulations applied. Moreover, this work can be extended to other possible applications such as wound healing and trauma monitoring.
LF suggested the idea, the design of the study, and the methodology. He also contributed to the writing of the manuscript. MA did the image processing and image analysis, and manuscript writing. JN did the coding and the mobile interface. AS and BM did the literature survey and conducted the measurements. MG contributed to the image processing part. All the authors read and approved the final manuscript.
This work is supported by Abu Dhabi University’s, Office of Research and Sponsored Programs.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Chieng M, Chuan O, Wee T, Joseph A, Mun Y, Hejar A. Prevalence of metabolic syndrome in type 2 diabetic patients: a comparative study using WHO, NCEP ATP III, IDF and Harmonized definitions. Sci Res. 2013;5(10):1689–96.Google Scholar
- World Health Organization. Global report on diabetes. 2016. http://www.who.int.
- Chanjuan L, Van Netten J, Van Baal J, Bus S, van der Heijden F. Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis. Biomed Opt. 2015;20(2):026003.View ArticleGoogle Scholar
- Apelqvist J, Larsson J. What is the most effective way to reduce incidence of amputation in the diabetic foot? Diabetes Metab Res Rev. 2000;16(1):S75–83.View ArticleGoogle Scholar
- Boulton A, Vileikyte L, Tennval G, Apelqvist J. The global burden of diabetic foot disease. Lancet. 2005;366:1719–24.View ArticleGoogle Scholar
- Rebolledo F, Teran T, Escobedo J. The pathogenesis of the diabetic foot ulcer: prevention and management. In: Dinh T. Global perspective on diabetic foot ulcerations. InTech. 2011. p. 155–182.Google Scholar
- Chanjuan L, van der Heijden F, Klein M, van Baal J, Bus S, van Netten J. Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study. Advanced biomedical and clinical diagnostic systems XI. SPIE. 2013; doi:10.1117/12.2001807.
- Diabetic foot problems: prevention and management. NICE Guidelines NG19. https://www.nice.org.uk/guidance/ng19. Accessed 26 August 2015.
- Bharara M, Schoess J, Armstrong D. Detecting inflammation in the acute diabetic foot and the foot in remission. Diabetes Metabo Res Rev. 2012;28(S1):15–20. doi: 10.1002/dmrr.2231.View ArticleGoogle Scholar
- Schaper N, van Netten J, Apelqvist J, Lipsky B, Bakker K. Prevention and management of foot problems in diabetes: a summary guidance for daily practice, based on the IWGDF guidance documents. Diabetes Metab Res Rev. 2016;32(S1):7–15. doi:10.1002/dmrr.2695.View ArticleGoogle Scholar
- Singh N, Armstrong D, Lipsky B. Preventing foot ulcers in patients with diabetes. JAMA. 2005;293(2):217–28.View ArticleGoogle Scholar
- Cajacuri L. Early diagnostic of diabetic foot using thermal images. HAL. https://tel.archives-ouvertes.fr/tel-01022921/document. Accessed 11 July 2014.
- Morbach S, Hartmann P. Diagnosis, treatment, and prevention of the diabetic foot syndrome. HARTMANN. Heidenheim D-89522; 2004.Google Scholar
- van Netten J, van Baal J, Chanjuan L, van der Heijden F, Bus S. Infrared thermal imaging for automated detection of diabetic foot complications. Diabetes Sci Technol. 2013;7(5):1122–9.View ArticleGoogle Scholar
- Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno J, Gonzalez-Bernal J. Diabetic foot and infrared thermography. Infrared Phy Technol. 2016;78:105–17.View ArticleGoogle Scholar
- Bharara M, Cobb JE, Claremont DJ. Thermography and thermometry in the assessment of diabetic neuropathic foot: a case for furthering the role of thermal techniques. Int J Low Extrem Wounds. 2006;5(4):250–60.View ArticleGoogle Scholar
- Cho S, Shin M, Kyung Y, Seo J, Lee Y, Park C, Chung J. Effects of infrared radiation and heat on human skin aging in vivo. Invest Dermatol Symp Proc. 2009;14:15–9.View ArticleGoogle Scholar
- Peregrina-Barreto H, Morales-Hernandez L, Rangel-Magdaleno J, Avina-Cervantes J, Ramirez-Cortes J, Morales-Caporal R. Quantitative estimation of temperature variations in plantar angiosomes: a study case for diabetic foot. Comput Math Methods Med. 2014. doi:10.1155/2014/585306.Google Scholar
- Vilcahuaman L, Harba R, Canals R, Zequera M, Wilches C, Arista M, Torres L, Arbanil H. Automatic analysis of plantar foot thermal images in at-risk Type II diabetes by using an infrared camera. IFMBE Proc. 2015;51:228–31.View ArticleGoogle Scholar
- FLIR Systems. FLIR ONE. http://www.flir.eu/flirone/ios-android. Accessed 16 June 2015.
- Curran A, Klein M, Hepokoski M, Packard C. Improving the accuracy of infrared measurements of skin temperature. Extreme Physiol Med. 2015. doi:10.1186/2046-7648-4-S1-A140.Google Scholar
- Moallem P, Razmjooy N. Optimal Threshold computing in automatic image thresholding using adaptive particle swarm optimization. J Appl Res Technol. 2012;10(5):703–12.Google Scholar
- Dongju L, Jian Y. Otsu method and K-means. IEEE Comp Soc. 2009. doi:10.1109/HIS.2009.74.Google Scholar
- MATLAB Image processing toolbox. MathWorks. http://www.mathworks.com/examples. Accessed 1994.
- Valsecchi A, Damas S, Santamaria J. Evolutionary intensity-based medical image registration: a review. Curr Med Imag Rev. 2013;9(4):283–97.View ArticleGoogle Scholar
- Medha V, Pardeep M, Hemant K. Image registration techniques: an overview. Int J Sig Process Image Process Pattern Recognit. 2009;2(3):11–28.Google Scholar
- Gonzalez R, Woods R. Digital image processing. 3rd ed. London: Pearson Education; 2008.Google Scholar