C.L. Herry

Quantitative assessment of pain-related thermal dysfunction through clinical digital infrared thermal imaging

BackgroundThe skin temperature distribution of a healthy human body exhibits a contralateral symmetry. Some nociceptive and most neuropathic pain pathologies are associated with an alteration of the thermal distribution of the human body. Since the dissipation of heat through the skin occurs for the most part in the form of infrared radiation, infrared thermography is the method of choice to study the physiology of thermoregulation and the thermal dysfunction associated with pain. Assessing thermograms is a complex and subjective task that can be greatly facilitated by computerised techniques.MethodsThis paper presents techniques for automated computerised assessment of thermal images of pain, in order to facilitate the physicians decision making. First, the thermal images are pre-processed to reduce the noise introduced during the initial acquisition and to extract the irrelevant background. Then, potential regions of interest are identified using fixed dermatomal subdivisions of the body, isothermal analysis and segmentation techniques. Finally, we assess the degree of asymmetry between contralateral regions of interest using statistical computations and distance measures between comparable regions.ResultsThe wavelet domain-based Poisson noise removal techniques compared favourably against Wiener and other wavelet-based denoising methods, when qualitative criteria were used. It was shown to improve slightly the subsequent analysis. The automated background removal technique based on thresholding and morphological operations was successful for both noisy and denoised images with a correct removal rate of 85% of the images in the database. The automation of the regions of interest (ROIs) delimitation process was achieved successfully for images with a good contralateral symmetry. Isothermal division complemented well the fixed ROIs division based on dermatomes, giving a more accurate map of potentially abnormal regions. The measure of distance between histograms of comparable ROIs allowed us to increase the sensitivity and specificity rate for the classification of 24 images of pain patients when compared to common statistical comparisons.ConclusionsWe developed a complete set of automated techniques for the computerised assessment of thermal images to assess pain-related thermal dysfunction.

Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences

The duration of a sit-to-stand (SiSt) transfer is a representative measure of a persons status of physical mobility. This paper measured the duration unobtrusively and automatically using a pressure sensor array under a bed mattress and a floor plate beside the bed. Pressure sequences were extracted from frames of sensor data measuring bed and floor pressure over time. The start time was determined by an algorithm based on the motion of the center of pressure (COP) on the mattress toward the front edge of the bed. The end time was determined by modeling the foot pressure exerted on the floor in the wavelet domain as the step response of a third-order transfer function. As expected, young and old healthy adults generated shorter SiSt durations of around 2.31 and 2.88 s, respectively, whereas post-hip fracture and post-stroke adults produced longer SiSt durations of around 3.32 and 5.00 s. The unobtrusive nature of pressure sensing techniques used in this paper provides valuable information that can be used for the ongoing monitoring of patients within extended-care facilities or within the smart home environment.

Analysis of breast thermography with an artificial neural network

Thermal imaging has been used for early breast cancer detection and risk prediction since the sixties. Examining thermograms for abnormal hyperthermia and hyper-vascularity patterns related to tumor growth is done by comparing images of contralateral breasts. Analysis can be tedious and challenging if the differences are subtle. The advanced computer technology available today can be utilized to automate the analysis and assist in decision-making. In our study, computer routines were used to perform ROI identification and image segmentation of infrared images recorded from 19 patients. Asymmetry analysis between contralateral breasts was carried out to generate statistics that could be used as input parameters to a backpropagation ANN. A simple 1-1-1 network was trained and employed to predict clinical outcomes based on the difference statistics of mean temperature and standard deviation. Results comparing the ANN output with actual clinical diagnosis are presented. Future work will focus on including more patients and more input parameters in the analysis. Performance of ANN network can be studied to select a set of parameters that would best predict the presence of breast cancer.

Preliminary results of severity of illness measures of rheumatoid arthritis using infrared imaging

For the first phase of a large project, we used an infrared imaging camera (thermograph) to obtain accurate measurements of body temperature in joints of twelve human normal subjects (control group) and for thirteen patients who had been diagnosed with rheumatoid arthritis (RA) by a rheumatologist. The ultimate goal is to create a low cost effective method to diagnose early synovitis. Temperature measurements of hands were analyzed with first order statistics. Results show significant temperature differences between control subjects and patients for every joint and hand portion measured. Future work will complete the analysis of knees, elbows, ankles, combine infrared (IR) imaging and intra-optical (IO) imaging, and incorporate feature extraction and classification approaches to stratify patients into severity of illness prior to, and after receiving treatment.

Digital processing techniques for the assessment of pain with infrared thermal imaging

The assessment of pain is a difficult topic that may be facilitated by the quantification of some of the phenomena usually involved in the sensation of pain, such as thermal abnormalities. Digital thermal imaging is a technique that records the skin temperature distribution of the body and thus can provide some insight in thermal dysfunction associated with pain. In this paper, we propose several digital processing steps to analyze thermal images of pain patients and summarize the results with a computer-aided decision-support system. Results from our analysis are expected to be validated by a statistical comparison with actual medical outcomes for the pain patients considered in the study.

Segmentation and landmark identification in infrared images of the human body

The segmentation and landmark identification in infrared images of the human body are key steps in a computerized processing of large database of thermal images. The segmentation task is especially challenging due to specific characteristics of thermal images. Few papers deal with segmentation techniques for clinical infrared images and available segmentation methods (e.g. for breast or military thermal images) do not perform well on other types of images. This paper presents a few strategies for the automated segmentation and registration of anatomical landmarks on thermal images of arms and hands. The segmentation method is based on mathematical morphological operations and simple rule based processing easily available through prior knowledge about the objects of interest

Search for abnormal thermal patterns in clinical thermal infrared imaging

This paper presents a technique to uncover abnormal thermal patterns in clinical thermal infrared images. When little information is known about pathological states, the search for abnormal thermal patterns is difficult. Supervised approaches require extensive knowledge about the distribution of abnormal patterns and are not appropriate for blind searches. We propose an approach that is based on a fusion of clusters from feature images and from an unsupervised clustering adaptive resonance theory (ART) neural network. We show that abnormal thermal patterns can be recovered in a selected number of controlled cases, where the number and location of anomalies are known. First results indicate the potential usefulness of our method for large scale screening of patients.

Segmentation of Infrared Images Using Cued Morphological Processing of Edge Maps

The segmentation of infrared images presents some unique challenges and depends greatly on the application considered. Methods developed for military FLIR or predictive maintenance applications do not perform well when translated to other applications such as infrared imaging of the human body. One solution to this problem is to use a small amount of available a priori information about the expected shape of the object to be recovered, in order to constrain the segmentation. In this paper, we present a new approach for segmenting infrared images in challenging environments, using a constrained morphological processing of edge maps. This method allows to recover weaker contours in noisy and non-uniform backgrounds. Results on synthetic and real objects show that the proposed approach outperforms other current infrared image segmentation algorithms for varying contrast and noise levels. Applications of our method include automated extraction and analysis of temperature variations in infrared image sequences with minimal or no interaction from a user.

Improving the Detection and Localization of Anatomical Landmark Points in Infrared Images Using Symmetry and Region Specific Constraints

This paper presents a new approach to detect and localize anatomical landmark points on clinical infrared images. It is based on a curvature analysis of contours and takes advantage of the symmetry of body regions to improve the localization of anatomical landmarks, based on region specific constraints. Better localization of anatomical landmarks is a key step in the registration and comparison of images over time, between patients and images from bilateral regions. It is also an important step in the design of automated thermal infrared imaging anomaly detection architecture.

Evolution of the Surface Temperature of Pianists' Arm Muscles Using Infrared Thermography

Musculoskeletal disorders are very frequent among musicians. Diagnosis is difficult due to the lack of objective tests and the multiplicity of symptoms. Treatment is also problematic and often requires that the musician stop playing. Most of these disorders are inflammatory in nature, and therefore involve temperature changes in the affected regions. Temperature measurements were recorded with an infrared camera. In this paper we present an overview of the temperature measurements made in the arms of 8 pianists during regular piano practice sessions