Uncertainty quantification in DenseNet model using myocardial infarction ECG signals

BACKGROUND AND OBJECTIVE

Myocardial infarction (MI) is a life-threatening condition diagnosed acutely on the electrocardiogram (ECG). Several errors, such as noise, can impair the prediction of automated ECG diagnosis. Therefore, quantification and communication of model uncertainty are essential for reliable MI diagnosis.

METHODS

A Dirichlet DenseNet model that could analyze out-of-distribution data and detect misclassification of MI and normal ECG signals was developed. The DenseNet model was first trained with the pre-processed MI ECG signals (from the best lead V6) acquired from the Physikalisch-Technische Bundesanstalt (PTB) database, using the reverse Kullback-Leibler (KL) divergence loss. The model was then tested with newly synthesized ECG signals with added em and ma noise samples. Predictive entropy was used as an uncertainty measure to determine the misclassification of normal and MI signals. Model performance was evaluated using four uncertainty metrics: uncertainty sensitivity (UNSE), uncertainty specificity (UNSP), uncertainty accuracy (UNAC), and uncertainty precision (UNPR); the classification threshold was set at 0.3.

RESULTS

The UNSE of the DenseNet model was low but increased over the studied decremental noise range (-6 to 24 dB), indicating that the model grew more confident in classifying the signals as they got less noisy. The model became more certain in its predictions from SNR values of 12 dB and 18 dB onwards, yielding UNAC values of 80% and 82.4% for em and ma noise signals, respectively. UNSP and UNPR values were close to 100% for em and ma noise signals, indicating that the model was self-aware of what it knew and didn't.

CONCLUSION

Through this work, it has been established that the model is reliable as it was able to convey when it was not confident in the diagnostic information it was presenting. Thus, the model is trustworthy and can be used in healthcare applications, such as the emergency diagnosis of MI on ECGs.

Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

A Comparative Study of Biomechanical and Geometrical Attributes of Abdominal Aortic Aneurysms in the Asian and Caucasian Populations

In this work, we provide a quantitative assessment of the biomechanical and geometric features that characterize abdominal aortic aneurysm (AAA) models generated from 19 Asian and 19 Caucasian diameter-matched AAA patients. 3D patient-specific finite element models were generated and used to compute peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (AWS) for each AAA. In addition, 51 global geometric indices were calculated, which quantify the wall thickness, shape, and curvature of each AAA. The indices were correlated with 99th WS (the only biomechanical metric that exhibited significant association with geometric indices) using Spearman's correlation and subsequently with multivariate linear regression using backward elimination. For the Asian AAA group, 99th WS was highly correlated (R2 = 0.77) with three geometric indices, namely tortuosity, intraluminal thrombus volume, and area-averaged Gaussian curvature. Similarly, 99th WS in the Caucasian AAA group was highly correlated (R2 = 0.87) with six geometric indices, namely maximum AAA diameter, distal neck diameter, diameter-height ratio, minimum wall thickness variance, mode of the wall thickness variance, and area-averaged Gaussian curvature. Significant differences were found between the two groups for ten geometric indices; however, no differences were found for any of their respective biomechanical attributes. Assuming maximum AAA diameter as the most predictive metric for wall stress was found to be imprecise: 24% and 28% accuracy for the Asian and Caucasian groups, respectively. This investigation reveals that geometric indices other than maximum AAA diameter can serve as predictors of wall stress, and potentially for assessment of aneurysm rupture risk, in the Asian and Caucasian AAA populations.

A computational intelligence tool for the detection of hypertension using empirical mode decomposition

Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.

Infrared (IR) thermography as a potential screening modality for carotid artery stenosis

In the present study, an infrared (IR) thermal camera was used to map the temperature of the target skin surface, and the resulting thermal image was evaluated for the presence of carotid artery stenosis (CAS). In the presence of stenosis in the carotid artery, abnormal temperature maps are expected to occur on the external skin surface, which could be captured and quantified using IR thermography. A Duplex Ultrasound (DUS) examination was used to establish the ground truth. In each patient, the background-subtracted thermal image, referred to as full thermal image, was used to extract novel parametric cold thermal feature images. From these images, statistical features, viz., correlation, energy, homogeneity, contrast, entropy, mean, standard deviation (SD), skewness, and kurtosis, were calculated and the two groups of patients (control and diseased: a total of 80 carotid artery samples) were classified. Both cut-off value- and support vector machine (SVM)-based binary classification models were tested. While the cut-off value classification model resulted in a moderate performance (70% accurate), SVM was found to have classified the patients with high accuracy (92% or higher). This preliminary study suggests the potential of IR thermography as a possible screening tool for CAS patients.

SVR ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram

In this paper, a continuous non-occluding blood pressure (BP) prediction method is proposed using multiple photoplethysmogram (PPG) signals. In the new method, BP is predicted by a committee machine or ensemble learning framework comprising multiple support vector regression (SVR) machines. The existing methods for continuous BP prediction rely on a single calibration model obtained from a single arterial segment. Our ensemble framework is the first BP estimation method which uses multiple SVR models for calibration from multiple arterial segments. This permits reducing of the mean prediction error and the risk of overfitting associated with a single model. Each SVR in the ensemble is trained on a comprehensive feature set that is constructed from a distinct PPG segment. The feature set includes pulse morphological parameters such as systolic pulse amplitude and area under the curve, heart rate variability (HRV) frequency, time domain parameters and the pulse wave velocity (PWV). Empirical evaluation using 40 volunteers with no serious health conditions shows that the proposed method is more reliable for estimating both the systolic and diastolic BP than similar methods employing a single calibration model under identical settings. Moreover, the combined output is found to be more stable than the output of any of the constituent models in the ensemble for both the systolic and diastolic cases.

99mTc-MAG3 diuresis renography in differentiating renal obstruction: Using statistical parameters as new quantifiable indices

OBJECTIVE

The aim of this study was to research, develop and assess the feasibility of using basic statistical parameters derived from renogram, "mean count value (MeanCV) and "median count value (MedianCV)", as novel indices in the diagnosis of renal obstruction through diuresis renography.

SUBJECTS AND METHODS

First, we re-digitalized and normalized 132 renograms from 74 patients in order to derive the MeanCV and MedianCV. To improve the performance of the parameters, we extrapolated renograms by a two-compartmental modeling. After that, the cutoff points for diagnosis using each modified parameter were set and the sensitivity and specificity were calculated in order to determine the best variants of MeanCV and MedianCV that could differentiate renal obstruction status into 3 distinct classes - i) unobstructed, ii) slightly obstructed, and iii) heavily obstructed.

RESULTS

The modified MeanCV and MedianCV derived from extended renograms predicted the severity of the renal obstruction. The most appropriate variants of MeanCV and MedianCV were found to be the MeanCV₅₀ and the MedianCV₆₀. The cutoff points of MeanCV₅₀ in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.50 and 0.72, respectively. The cutoff points of MedianCV₆₀ in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.35 and 0.69, respectively. Notably, MeanCV₅₀ and MedianCV₆₀ were not significantly influenced by either age or gender.

CONCLUSIONS

The MeanCV₅₀ and the MedianCV₆₀ derived from a renogram could be incorporated with other quantifiable parameters to form a system that could provide a highly accurate diagnosis of renal obstructions.

A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer's disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

Physical mechanism and modeling of heat generation and transfer in magnetic fluid hyperthermia through Néelian and Brownian relaxation: a review

Current clinically accepted technologies for cancer treatment still have limitations which lead to the exploration of new therapeutic methods. Since the past few decades, the hyperthermia treatment has attracted the attention of investigators owing to its strong biological rationales in applying hyperthermia as a cancer treatment modality. Advancement of nanotechnology offers a potential new heating method for hyperthermia by using nanoparticles which is termed as magnetic fluid hyperthermia (MFH). In MFH, superparamagnetic nanoparticles dissipate heat through Néelian and Brownian relaxation in the presence of an alternating magnetic field. The heating power of these particles is dependent on particle properties and treatment settings. A number of pre-clinical and clinical trials were performed to test the feasibility of this novel treatment modality. There are still issues yet to be solved for the successful transition of this technology from bench to bedside. These issues include the planning, execution, monitoring and optimization of treatment. The modeling and simulation play crucial roles in solving some of these issues. Thus, this review paper provides a basic understanding of the fundamental and rationales of hyperthermia and recent development in the modeling and simulation applied to depict the heat generation and transfer phenomena in the MFH.

An integrated index for automated detection of infarcted myocardium from cross-sectional echocardiograms using texton-based features (Part 1)

Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification.

Theoretical study of pre-formed hole geometries on femtosecond pulse energy distribution in laser drilling

Maxwell's wave equation was solved for fs laser drilling of silicon. The pre-formed hole wall's influence on the propagation behavior of subsequent laser pulses was investigated. The laser intensity at hole bottom shows distinct profile as compared with that at hole entrance. The multi-peaks and ring structure of the laser intensity were found at hole bottom. The position of maximum laser intensity (MLI) in relation to the wall taper angle was studied. It was found that the position of the MLI point would be closer to the hole entrance with increasing taper angle. This observation provides valuable information in predicting the position of plasma plume which is a key factor influencing laser drilling process. The elliptical entrance hole shape and zonal structure at the hole bottom reported in the literatures have been reasonably explained using the laser intensity distribution obtained in the present model.

Three-dimensional CFD/MRI modeling reveals that ventricular surgical restoration improves ventricular function by modifying intraventricular blood flow

Surgical ventricular restoration (SVR) is designed to normalize distorted ventricular shape and size in patients with left ventricular (LV) dysfunction and akinetic and dyskinetic segments. This study is aimed to quantify the characteristics of LV as a pump for a case before and after SVR, which is followed by coronary artery bypass grafting (CABG). We hypothesize that SVR+CABG improves heart flow. A patient with heart failure had magnetic resonance (MR) scans before and 4 months after SVR. LV endocardial geometries were semi-automated segmented and reconstructed using our customized algorithm. The arbitrary Lagrangian-Eulerian formulation of Navier-Stokes equations was solved to derive the flow patterns and calculate pressure differences in LV. After SVR, LV ejection fraction increased from 34% to 48% in patient but was still lower than normal (70%). Second, LV vortices were stronger than pre-surgery but still weaker than normal. The maximum pressure differences between ventricular base and apex increased from 180 to 400 Pa during diastole, from 252 to 560 Pa during systole, respectively. As anticipated, SVR reduced LV volumes and augmented LV ejection fraction. Three-dimensional CFD/MRI modeling suggests that improved diastolic and systolic ventricular function after SVR is associated with changes in intraventricular blood flow.

Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

Level set method for segmentation of infrared breast thermograms

Breast thermography is a physiological test that provides information based on the temperature changes in breast. It records the temperature distribution of a body using the infrared radiation emitted by the surface of that body. Precancerous tissue and the area around a cancerous tumor have higher temperature due to angiogenesis, and higher chemical and blood vessel activity than a normal breast; hence breast thermography has potential to detect early abnormal changes in breast tissues. It can detect the first sign of forming up cancer before mammography can detect. The thermal information can be shown in a pseudo colored image where each color represents a specific range of temperature. Various methods can be applied to extract hot regions for detecting suspected regions of interests in the breast infrared images and potentially suspicious tissues. Image segmentation techniques can play an important role to segment and extract these regions in the breast infrared images. Shape, size and borders of the hottest regions of the images can help to determine features which are used to detect abnormalities. In this paper, three image segmentation methods: k-means, fuzzy c-means and level set are discussed and compared. These three methods are tested for different cases such as fibrocystic, inflammatory cancer cases. The hottest regions of thermal breast images in all cases are extracted and compared to the original images. According to the results, level set method is a more accurate approach and has potential to extract almost exact shape of tumors.

Computer-aided diagnosis of diabetic retinopathy: a review

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.

Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation

The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness. The identification of retinal anatomical regions is a prerequisite for the computer-aided diagnosis of several retinal diseases. The manual examination of optic disk (OD) is a standard procedure used for detecting different stages of DR and glaucoma. In this article, a novel automated, reliable, and efficient OD localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the OD due to fuzzy boundaries, inconsistent image contrast, or missing edge features. This article proposes a novel and probably the first method using the Attanassov intuitionistic fuzzy histon (A-IFSH)–based segmentation to detect OD in retinal fundus images. OD pixel intensity and column-wise neighborhood operation are employed to locate and isolate the OD. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous, and 31 DR images. Our proposed method has yielded precision of 0.93, recall of 0.91, F-score of 0.92, and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with the Otsu and gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

CFD simulation of flow through heart: a perspective review

The heart is an organ which pumps blood around the body by contraction of muscular wall. There is a coupled system in the heart containing the motion of wall and the motion of blood fluid; both motions must be computed simultaneously, which make biological computational fluid dynamics (CFD) difficult. The wall of the heart is not rigid and hence proper boundary conditions are essential for CFD modelling. Fluid-wall interaction is very important for real CFD modelling. There are many assumptions for CFD simulation of the heart that make it far from a real model. A realistic fluid-structure interaction modelling the structure by the finite element method and the fluid flow by CFD use more realistic coupling algorithms. This type of method is very powerful to solve the complex properties of the cardiac structure and the sensitive interaction of fluid and structure. The final goal of heart modelling is to simulate the total heart function by integrating cardiac anatomy, electrical activation, mechanics, metabolism and fluid mechanics together, as in the computational framework.

Evaluation of tear evaporation from ocular surface by functional infrared thermography

PURPOSE

A novel technique was developed to measure tear evaporation and monitor its variation with respect to time, for the studying of ocular physiology based on dynamic functional infrared thermography and the first law of thermodynamics using the measured ocular surface temperatures (OSTs). This is a noninvasive, noncontact temperature measuring method that is widely applied in the field of biomedicine.

METHODS

A simple method based on the ocular thermal data was proposed to measure the rate of tear evaporation. The OST of 60 normal subjects were recorded in the form of sequential thermal images. For each thermal sequence, the ocular region was selected and warped to a standard form. Thermal data within the regions were processed, on the basis of the first law of thermodynamics to derive the evaporation rate.

RESULTS

For elder subjects (aged above 35), the rate was determined to be 55.82 Wm(-2) and for younger subjects, the rate was 58.9 Wm(-2). The corneal rate of evaporation in elder subjects was found statistically (p < 0.11) larger than their younger counterparts. The rate of blinking was observed to be related to the variation of evaporation rate.

CONCLUSIONS

The authors have measured the evaporation rate on a sequence of thermographic images. A region of interest was selected at first and the same region on all the images were warped into a standard form. Calculations were performed based on the thermal data in those regions to obtain the values of interest. The authors found that the tear evaporation rate for subjects of all age groups was 57.36 +/- 12.73 Wm(-2) and the corneal tear evaporation was higher in elder subjects. The corneal rate of evaporation fluctuated in a larger magnitude in subjects who blinked more than average.

Estimating the mutual information between bilateral breast in thermograms using nonparametric windows

Comparison between contra lateral breast images is one of the effective methods in breast cancer detection. Asymmetric temperature distribution can be an indicator of abnormality. The mutual information is a good measure of nonlinear correlation. It is a measure that captures linear and nonlinear dependencies, without requiring the specification of any kind of model of dependence. Therefore, it is suitable for our abnormality indicator. Although nonparametric windows is a numerically expensive technique but it is accurate. The reason is that nonparametric windows incorporate an interpolation model which enhances the resolution to a highly oversampled image. For our purposes we worked with sixty simulated breast thermal images. It is shown that the more similar the thermal image of right breast to the thermal image of left breast, the closer the normalized mutual information value to one.

Data mining approach to evaluating the use of skin surface electropotentials for breast cancer detection

The Biofield Diagnostic System (BDS) uses a score formed with measured skin surface electropotentials and a prior Level Of Suspicion (LOS) value (predicted by the physician based on the patient's ultrasound or mammography results) to calculate a revised Post-BDS LOS to indicate the presence of breast cancer. The demographic details, BDS test results, and the recorded electropotential values form a potentially useful dataset, which can be further explored with data mining tools to extract important information that can be used to improve the current predictive accuracy of the device. According to the proposed data mining framework, the BDS dataset with 291 cases was first pre-processed to remove outliers and then used to select relevant and informative features for classifier development and finally to evaluate the capability of the built classifiers in detecting the presence of the disease. Two popular feature selection techniques, namely, the filter and wrapper methods, were used in parallel for feature selection. A few statistical inference based classifiers and neural networks were used for classification. The proposed technique significantly improved the BDS prediction accuracy. Also, the use of prior LOS and, hence, the Post-BDS LOS, associates a mild subjective interpretation to the current prediction methodology used by BDS. However, the feature subset selected in our analysis that gave the best accuracy did not use either of these features. This result indicates the possibility of using BDS as a better objective assessment tool for breast cancer detection.

Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review

Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.

Prediction and parametric analysis of thermal profiles within heated human skin using the boundary element method

In this paper, an axisymmetric model of the human skin is developed to simulate the steady-state temperature distribution during contact with a hot solid. Simulations are carried out using the boundary element method. This study seeks to investigate the feasibility of using the boundary element method in the studies of burn. A sensitivity analysis is carried out to examine the effects of various parameters on the temperature distribution inside the skin during burn. Furthermore, a statistical analysis based on the Taguchi method is performed to determine the combination of factors that produce the desired outcome (least increase in temperature). In order to validate the accuracy of the numerical scheme, results obtained using the boundary element method are compared with the solutions obtained using the more established finite-element method.

Computer-based detection of diabetes retinopathy stages using digital fundus images

Diabetes mellitus is a heterogeneous clinical syndrome characterized by hyperglycaemia and the long-term complications are retinopathy, neuropathy, nephropathy, and cardiomyopathy. It is a leading cause of blindness. Diabetic retinopathy is the progressive pathological alterations in the retinal microvasculature, leading to areas of retinal nonperfusion, increased vascular permeability, and the pathological proliferation of retinal vessels. Hence, it is beneficial to have regular cost-effective eye screening for diabetes subjects. Nowadays, different stages of diabetes retinopathy are detected by retinal examination using indirect biomicroscopy by senior ophthalmologists. In this work, morphological image processing and support vector machine (SVM) techniques were used for the automatic diagnosis of eye health. In this study, 331 fundus images were analysed. Five groups were identified: normal retina, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. Four salient features blood vessels, microaneurysms, exudates, and haemorrhages were extracted from the raw images using image-processing techniques and fed to the SVM for classification. A sensitivity of more than 82 per cent and specificity of 86 per cent was demonstrated for the system developed.

The use of skin surface electropotentials for breast cancer detection--preliminary clinical trial results obtained using the biofield diagnostic system

The purpose of this study is to evaluate the efficiency of the Biofield Diagnostic System (BDS) as an adjunct to established diagnostic techniques such as mammography and ultrasound in differentiating benign and malignant breast lesions. The clinical trial was conducted at the Tan Tock Seng hospital, Singapore. 103 women scheduled for mammography and/or ultrasound tests participated in the study. The BDS test recorded a sensitivity of 100%, specificity of 97.6%, and an accuracy of 98.1%. The area under the ROC curve was 0.988 which was slightly lower than that of ultrasound (0.994) and slightly higher than that of mammography (0.951). The BDS test has demonstrated high sensitivity and specificity values in the studied population. The accuracy is also comparable to that of diagnostic techniques like mammography and ultrasound. Thus, it is evident that BDS can be a fast and reliable adjunct tool for getting a secondary opinion on lesions with indeterminate mammographic and sonographic results.

A perspective on medical infrared imaging

Since the early days of thermography in the 1950s, image processing techniques, sensitivity of thermal sensors and spatial resolution have progressed greatly, holding out fresh promise for infrared (IR) imaging techniques. Applications in civil, industrial and healthcare fields are thus reaching a high level of technical performance. The relationship between body temperature and disease was documented since 400 bc. In many diseases there are variations in blood flow, and these in turn affect the skin temperature. IR imaging offers a useful and non-invasive approach to the diagnosis and treatment (as therapeutic aids) of many disorders, in particular in the areas of rheumatology, dermatology, orthopaedics and circulatory abnormalities. This paper reviews many usages (and hence the limitations) of thermography in biomedical fields.

Analysis of nano drug carriers towards optimum release rate

The aim of this paper is to determine which polymer shape (sphere, hemisphere, cylinder, tablet, cuboid, tetrahedron or octahedron) is best for zero kinetics drug delivery and for sustained nanoparticle release. We applied the Carslaw and Jaeger heat diffusion equations of a sphere with same order of its effective surface area to volume ratio as a reference, to predict how drug delivery would occur in other shapes. The assumption of the heat diffusion analogy in the present study of negligible drug particles is sensible since the drug at nano scale is tiny and thus nearly 'massless'. From tests involving changing the micro-carrier configuration, we can confirm that shape is an important factor to consider when examining drug release rates, to achieve zero-order design. The preliminary analysis suggests that a hemisphere shape is more promising in achieving zero-order drug release rate, followed by a tablet shape of L = 2R(s), 3R(s), a tetrahedron, a cylindrical shape with L = 3R(s), 2R(s), a sphere, a cuboid shape with L = 3R(s), 2R(s), and finally an octahedron. This is due to the larger effective surface area, given the same parameters and surrounding conditions. In other words, a hemisphere shape reaches zero order in the shortest possible time and thus permits sustained zero-order particle release rate. Based on the ratio between the surface area of a micro-carrier and its volume, we further derived the drug release equation of cylinder/tablet shaped micro-carrier. By introducing h as an index of the similarity of the drug release rate to a desirable zero-order drug release rate, we obtained a relationship between different length/radius (L/R) values of cylinder/tablet shapes and the index h. From this relationship, we find the best L/R ratio that can achieve a drug release process most similar to a zero-order drug release process. Future work is to include optimization of the lipid matrixes.

Advanced integrated technique in breast cancer thermography

Thermography is a passive and non-contact imaging technique used extensively in the medical arena, but in relation to breast care, it has not been accepted as being on a par with mammography. This paper proposes the analysis of thermograms with the use of artificial neural networks (ANN) and bio-statistical methods, including regression and receiver operating characteristics (ROC). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be achieved. The suggested method is a multi-pronged approach comprising of linear regression, radial basis function network (RBFN) and ROC analysis. It is a novel, integrative and powerful technique that can be used to analyse large amounts of complicated measured data such as temperature values extracted from abnormal and healthy breast thermograms. The use of regression allows the correlation between the variables and the actual health status of the subject, which is decided by other traditional means such as the gold standard of mammography for breast cancer detection. This is important as it helps to select the appropriate variables to be used as inputs for building the neural network. RBFN is next trained to produce the desired outcome that is either positive or negative. When this is done, the RBFN possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training of superior classification and decision-making abilities as compared to other networks such as backpropagation. Lastly, ROC is applied to evaluate the sensitivity, specificity and accuracy of the outcome for the RBFN test files. The proposed technique has an accuracy rate of 80.95%, with 100% sensitivity and 70.6% specificity in identifying breast cancer. The results are promising as compared to clinical examination by experienced radiologists, which has an accuracy rate of approximately 60-70%. To sum up, technological advances in the field of infrared thermography over the last 20 years warrant a re-evaluation of the use of high-resolution digital thermographic camera systems in the diagnosis and management of breast cancer. Thermography seeks to identify the presence of a tumour by the elevated temperature associated with increase blood flow and cellular activity. Of particular interest would be investigation in younger women and men, for whom mammography is either unsuitable or of limited effectiveness. The paper evaluated the high-definition digital infrared thermographic technology and knowledge base; and supports the development of future diagnostic and therapeutic services in breast cancer imaging. Through the use of integrative ANN and bio-statistical methods, advances are made in thermography application with regard to achieving a higher level of consistency. For breast cancer care, it has become possible to use thermography as a powerful adjunct and biomarker tool, together with mammography for diagnosis purposes.

Boundary element method with bioheat equation for skin burn injury

Burns are second to vehicle crashes as the leading cause of non-intentional injury deaths in the United States. The survival of a burn patient actually depends on the seriousness of the burn. It is important to understand the physiology of burns for a successful treatment of a burn patient. This has prompted researchers to conduct investigations both numerically and experimentally to understand the thermal behaviour of the human skin when subjected to heat injury. In this study, a model of the human skin is developed where the steady state temperature during burns is simulated using the boundary element method (BEM). The BEM is used since it requires boundary only discretion and thus, reduces the requirement of high computer memory. The skin is modeled as three layered in axisymmetric coordinates. The three layers are the epidermis (uppermost), dermis (middle) and subcutaneous fat. Burning is applied via a heating disk which is assumed to be at constant temperature. The results predicted by the BEM model showed very good agreement with the results obtained using the finite element method (FEM). The good agreement despite using only linear elements as compared to quadratic elements in the FEM model shows the versatility of the BEM. A sensitivity analysis was conducted to investigate how changes in the values of certain skin variables such as the thermal conductivity and environmental conditions like the ambient convection coefficient affect the temperature distribution inside the skin. The Taguchi method was also applied to identify the combination of parameters which produces the largest increase in skin temperature during burns.

Application of higher order spectra for the identification of diabetes retinopathy stages

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.