Detection of cancerous breasts by dynamic area telethermometry

Skin surface infrared thermography in pressure ulcer outcome prognosis

OBJECTIVE

To assess the usefulness of skin surface infrared thermography (SSIT) as a prognostic tool in the treatment of stages III and IV pressure ulcers (PU), with hydrocolloid/hydrogel dressings plus 20 exposures to low-level laser therapy (LLLT), compared with hydrocolloid dressings alone, in a group of long-term bedbound care patients.

METHOD

In this comparative study, participants were randomly assigned to group I: PUs treated with specialist wound dressings and laser therapy, or to group II: PUs treated with specialist wound dressings without laser therapy. Thermal imaging sessions were carried out at the beginning of the study, and after two and four weeks of treatment. Thermal imaging processing was applied to compare percentage differences in the temperature distribution between the groups within selected regions of interest (ROIs). The correlation between the temperature distribution and PU healing was evaluated.

RESULTS

A total of 43 patients took part. In the study, three variants of PU healing were observed: pure healing (H) with minimal granulation; healing with hypergranulation (H+G); and non-healing (NH). Analyses of SSIT-related thermographic patterns revealed their dependence on the course of healing. The percentage of successful PU healing reached 79.2% in group I compared with 73.7% in group II (p<0.05) The dominant variant of healing in Group I was H, while in group II the variants H and H+G were present with equal frequency.

CONCLUSION

Thermal imaging processing allowed comparison of differences in the temperature distribution between the groups within ROIs. Application of LLLT significantly improved the healing process (p<0.05). The clinical significance of this finding should be confirmed with larger studies; however, SSIT may be useful as a prognostic tool during the treatment of PUs, with the ability to predict the course of healing initially, that is independent of LLLT treatment.

A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography

Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.

An automated approach to enhance the thermographic evaluation on orofacial regions in lateral facial thermograms

Segmentation of characteristic facial regions has often been an initial step of thermographic analysis in face recognition and clinical diagnosis. Moreover, fast and accurate thermographic analysis on characteristic areas is highly reliant on segmentation approach. Usually, frontal and lateral projections are used to capture the facial thermograms. The significant role of lateral facial thermography to diagnose the various problems associated with orofacial regions has been remarkable in many studies. So far, no study has presented an automatic approach for the segmentation of characteristic areas in lateral facial thermograms. For this purpose, an automatic approach to segment the characteristic areas in lateral facial thermograms is proposed. The dataset of 140 facial thermograms with 1 left and 1 right lateral view per subject is created. Initially, image binarization is performed using optimal temperature thresholding for better visualization of facial geometry. Then, the automatic localization of characteristic points is performed at two levels, based on (a) geometrical features of the face, and (b) local thermal patterns. At last, the characteristic points and auxiliary points are used to segment the characteristic areas in the orofacial region of the face. To evaluate the segmentation performance of the proposed methodology, the automatically localized characteristic points are compared with manually marked using Euclidean distance based comparison criterion. With the localization error δ_{ch_pt}≤0.05, the proposed automatic approach shows 92.04% of overall localization accuracy and 85% of overall segmentation accuracy. The key conclusion is that the proposed algorithm can potentially automate the process of thermographic analysis on characteristic areas to assist the diagnosis of problems in the orofacial region.

Thermal Imaging in Medical Science

Thermal imaging is a non-destructive, non-contact and rapid system. It reports temperature through measuring infrared radiation emanated by an object/ material surface. Automated thermal imaging system involves thermal camera equipped with infrared detectors, signal processing unit and image acquisition system supported by computer. It is elaborated in wide domains applications. Extensive focus is directed to the thermal imaging in the medical domain especially breast cancer detection. This chapter provided the main concept and the different applications of thermal imaging. It explores and analyses several works in the light of studding the thermograph. It is an effective screening tool for breast cancer prediction. Studies justify that thermography can be considered a complementary tool to detect breast diseases. The current chapter reviews many usages and limitations of thermography in biomedical field. Extensive recommendations for future directions are summarized to provide a structured vision of breast thermography.

Hybrid analysis for indicating patients with breast cancer using temperature time series

Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an average accuracy of 95.38% was obtained.

A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification

OBJECTIVES

Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography.

METHODS AND MATERIAL

In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making.

RESULTS

For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms.

CONCLUSIONS

Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups.

Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis

Breast cancer is the most common type of cancer among women and despite recent advances in the medical field, there are still some inherent limitations in the currently used screening techniques. The radiological interpretation of screening X-ray mammograms often leads to over-diagnosis and, as a consequence, to unnecessary traumatic and painful biopsies. Here we propose a computer-aided multifractal analysis of dynamic infrared (IR) imaging as an efficient method for identifying women with risk of breast cancer. Using a wavelet-based multi-scale method to analyze the temporal fluctuations of breast skin temperature collected from a panel of patients with diagnosed breast cancer and some female volunteers with healthy breasts, we show that the multifractal complexity of temperature fluctuations observed in healthy breasts is lost in mammary glands with malignant tumor. Besides potential clinical impact, these results open new perspectives in the investigation of physiological changes that may precede anatomical alterations in breast cancer development.

Motion tracking in infrared imaging for quantitative medical diagnostic applications

In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.

A pruned ensemble classifier for effective breast thermogram analysis

Thermal infrared imaging has been shown to be useful for diagnosing breast cancer, since it is able to detect small tumors and hence can lead to earlier diagnosis. In this paper, we present a computer-aided diagnosis approach for analysing breast thermograms. We extract image features that describe bilateral differences of the breast regions in the thermogram, and then feed these features to an ensemble classifier. For the classification, we present an extension to the Under-Sampling Balanced Ensemble (USBE) algorithm. USBE addresses the problem of imbalanced class distribution that is common in medical decision making by training different classifiers on different subspaces, where each subspace is created so as to resemble a balanced classification problem. To combine the individual classifiers, we use a neural fuser based on discriminants and apply a classifier selection procedure based on a pairwise double-fault diversity measure to discard irrelevant and similar classifiers. We demonstrate that our approach works well, and that it statistically outperforms various other ensemble approaches including the original USBE algorithm.

Involuntary motion tracking for medical dynamic infrared thermography using a template-based algorithm

In medical applications, Dynamic Infrared (IR) Thermography is used to detect the temporal variation of the skin temperature. Dynamic Infrared Imaging first introduces a thermal challenge such as cooling on the human skin, and then a sequence of hundreds of consecutive frames is acquired after the removal of the thermal challenge. As a result, by analyzing the temporal variation of the skin temperature over the image sequence, the thermal signature of skin abnormality can be examined. However, during the acquisition of dynamic IR imaging, the involuntary movements of patients are unavoidable, and such movements will undermine the accuracy of diagnosis. In this study, based on the template-based algorithm, a tracking approach is proposed to compensate the motion artifact. The affine warping model is adopted to estimate the motion parameter of the image template, and then the Lucas-Kanade algorithm is applied to search for the optimized parameters of the warping function. In addition, the weighting mask is also incorporated in the computation to ensure the robustness of the algorithm. To evaluate the performance of the approach, two sets of IR image sequences of a subject's hand are analyzed: the steady-state image sequence, in which the skin temperature is in equilibrium with the environment, and the thermal recovery image sequence, which is acquired after cooling is applied on the skin for 60 seconds. By selecting the target region in the first frame as the template, satisfactory tracking results were obtained in both experimental trials, and the robustness of the approach can be effectively ensured in the recovery trial.

NADINE: new approaches to detecting breast cancer by sequential μm-wavelength imaging with the aid of novel frequency analysis techniques

The study focuses on 12 breasts of six breast cancer patients sequential µm-wavelength imaging, taken by two different 3-5 μm wavelength area indium antimony (InSb) photovoltaic cameras. The aim of the study was to compare the functionality of area and pixel-based frequency analyses. Comparisons between these frequency analysis methods were made according to their relevancy to mammographic findings. Another objective of the study was to find reliable imaging conditions by specifying the border conditions for the patient stabilizing imaging bed and managing the imaging situation. According to the results, the match of pixel based frequency analysis to the mammography findings is better than using area frequency analysis. The results also indicate that when the optical axis of the camera in relation to the surface of the breast to be imaged grows to more than 40°, the emissivity changes dramatically and at that point reliable results will not be obtained. Consequently the analysis of the imagined breast requires more images to be fused into one analysis image to cover the whole breast.

Sensitivity and specificity of long wave infrared imaging for attention-deficit/hyperactivity disorder

OBJECTIVE

This study was the first to investigate the efficacy of long wave infrared (LWIR) imaging as a diagnostic tool for ADHD.

METHOD

This study was conducted to assess the sensitivity and specificity of LWIR imaging as a method of diagnosis among 190 patients (ages 4.4-57 years) with various diagnoses, including ADHD, who came into our office for neuropsychological evaluation.

RESULTS

LWIR imaging demonstrated a moderate level of sensitivity (65.71%) in identifying patients with ADHD and a high level of specificity (94%) in discriminating those with ADHD from those with other diagnoses. The overall classification rate was 73.16%. This was indicative of a high level of discriminant validity in distinguishing between patients with and without ADHD. There was a moderate level of agreement between LWIR imaging and multiple other diagnostic tests for ADHD.

CONCLUSIONS

LWIR imaging demonstrated high sensitivity and specificity as a diagnostic tool for ADHD. These results provide evidence for the efficacy of a novel, quick, and effective way to investigate the physiological basis of one of the most prevalent childhood psychiatric disorders.

Imaging of breast cancer with mid- and long-wave infrared camera

In this novel study the breasts of 15 women with palpable breast cancer were preoperatively imaged with three technically different infrared (IR) cameras - micro bolometer (MB), quantum well (QWIP) and photo voltaic (PV) - to compare their ability to differentiate breast cancer from normal tissue. The IR images were processed, the data for frequency analysis were collected from dynamic IR images by pixel-based analysis and from each image selectively windowed regional analysis was carried out, based on angiogenesis and nitric oxide production of cancer tissue causing vasomotor and cardiogenic frequency differences compared to normal tissue. Our results show that the GaAs QWIP camera and the InSb PV camera demonstrate the frequency difference between normal and cancerous breast tissue; the PV camera more clearly. With selected image processing operations more detailed frequency analyses could be applied to the suspicious area. The MB camera was not suitable for tissue differentiation, as the difference between noise and effective signal was unsatisfactory.

Dynamic infrared imaging in identification of breast cancer tissue with combined image processing and frequency analysis

Five combinations of image-processing algorithms were applied to dynamic infrared (IR) images of six breast cancer patients preoperatively to establish optimal enhancement of cancer tissue before frequency analysis. mid-wave photovoltaic (PV) IR cameras with 320x254 and 640x512 pixels were used. The signal-to-noise ratio and the specificity for breast cancer were evaluated with the image-processing combinations from the image series of each patient. Before image processing and frequency analysis the effect of patient movement was minimized with a stabilization program developed and tested in the study by stabilizing image slices using surface markers set as measurement points on the skin of the imaged breast. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. The ability of each combination to locate the mammography finding of breast cancer in each patient was compared. Our results show that data collected with a 640x512-pixel mid-wave PV camera applying image-processing methods optimizing signal-to-noise ratio, morphological image processing and linear image restoration before frequency analysis possess the greatest superiority value, showing the cancer area most clearly also in the match centre of the mammography estimation.

Automated image segmentation for breast analysis using infrared images

In order to realize a fully automated thermogram analysis package for breast cancer detection, it is necessary to identify the region of interest in the thermal image prior to analysis. A nearly fully automated approach is outlined that is able to successfully locate the breast regions in most of the images analyzed. The approach consists of a sequence of Canny edge detectors to determine the body boundaries and to isolate the most likely candidates for the bottom breast boundary. Three different strategies for identifying the bottom breast boundary are investigated: a variation of the Hough transform to identify the curved edges in the image, an algorithm used to detect the longest connected edges that are not part of the body boundary, and a third approach involving the density of detected edges in the breast region. The last two methods show great promise in successfully segmenting the breasts.

Evaluation of different marker sets for motion artifact reduction in breast dynamic infrared imaging

Dynamic infrared imaging is a promising technique to be applied to early breast cancer diagnosis. It is based on the acquisition of hundreds of consecutive thermal images with a frame rate ranging from 50 to 200 frames/s, followed by the spectral analysis of temperature time series at each image pixel. To improve the time series signal-to-noise ratio, it is useful to realign the thermal images of the acquisition sequence. Our previous studies demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we evaluate the performance of different marker sets by means of a model that allows estimating the signal-to-noise ratio increment due to registration, and we conclude that a 9-marker set is a good compromise between motion artifact reduction and the time required to prepare the patient.

Dynamic infrared imaging for the detection of malignancy

The potential for malignancy detection using dynamic infrared imaging (DIRI) has been investigated in an animal model of human malignancy. Malignancy was apparent in images formed at the vasomotor and cardiogenic frequencies of tumour bearing mice. The observation of malignancy was removed by the administration of an agent that blocks vasodilation caused by nitric oxide (NO). Image patterns similar to those that characterize malignancy could be mimicked in normal mice using an NO producing agent. Apparently DIRI allows for cancer detection in this model through vasodilation caused by malignancy generated NO. Dynamic infrared detection of vasomotor and cardiogenic surface perfusion was validated in human subjects by a comparison with laser Doppler flowmetry (LDF). Dynamic infrared imaging technology was then applied to breast cancer detection. It is shown that dynamic infrared images formed at the vasomotor and cardiogenic frequencies of the normal and malignant breast have image pattern differences, which may allow for breast cancer detection.

Vision of the future: initial experience with intraoperative real-time high-resolution dynamic infrared imaging. Technical note

High-resolution dynamic infrared (DIR) imaging provides intraoperative real-time physiological, anatomical, and pathological information; however, DIR imaging has rarely been used in neurosurgical patients. The authors report on their initial experience with intraoperative DIR imaging in 30 such patients. A novel, long-wave (8-10 microm), narrow-band, focal-plane-array infrared photodetector was incorporated into a camera system with a temperature resolution of 0.006 degrees C, providing 65,000 pixels/frame at a data acquisition rate of 200 frames/second. Intraoperative imaging of patients was performed before and after surgery. Infrared data were subsequently analyzed by examining absolute differences in cortical temperatures, changes in temperature over time, and infrared intensities at varying physiological frequencies. Dynamic infrared imaging was applied in a variety of neurosurgical cases. After resection of an arteriovenous malformation, there was postoperative hyperperfusion of the surrounding brain parenchyma, which was consistent with a loss of autoregulation. Bypass patency and increased perfusion of adjacent brain were documented during two of three extracranial-intracranial bypasses. In seven of nine patients with epilepsy the results of DIR imaging corresponded to seizure foci that had been electrocorticographically mapped preoperatively. Dynamic infrared imaging demonstrated the functional cortex in four of nine patients undergoing awake resection and cortical stimulation. Finally, DIR imaging exhibited the distinct thermal footprints of 14 of 16 brain tumors. Dynamic infrared imaging may prove to be a powerful adjunctive intraoperative diagnostic tool in the neurosurgical imaging armamentarium. Real-time assessment of cerebral vessel patency and cerebral perfusion are the most direct applications of this technology. Uses of this imaging modality in the localization of epileptic foci, identification of functional cortex during awake craniotomy, and determination of tumor border and intraoperative brain shift are avenues of inquiry that require further investigation.

Assessment of physiologic and pathologic radiative heat dissipation using dynamic infrared imaging

This paper reviews the mechanism and assessment of regulated radiative heat dissipation, involving the circulatory system and the skin. It describes the quantitative assessment of skin temperature modulation. The main regulating process, which can be quantitatively monitored by fast and sensitive dynamic infrared imaging, involves autonomic nervous control of cutaneous and subcutaneous perfusion. This control is significantly affected by a variety of local or systemic pathologic conditions, including cancer and certain neuropathies. A potential clinical application that objectively assesses local attenuation of temperature modulation in the presence of breast cancer is described in some detail. Systemic aberrations in skin temperature modulation can be clinically useful also in neurology. It can be used also in psychology and psychiatry to evaluate transient effects of mental stress on the autonomic nervous system.