Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features.

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

Retrospective non-uniform illumination correction techniques in images of tuberculosis.

Image pre-processing is highly significant in automated analysis of microscopy images. In this work, non-uniform illumination correction has been attempted using the surface fitting method (SFM), multiple regression method (MRM), and bidirectional empirical mode decomposition (BEMD) in digital microscopy images of tuberculosis (TB). The sputum smear positive and negative images recorded under a standard image acquisition protocol were subjected to illumination correction techniques and evaluated by error and statistical measures. Results show that SFM performs more efficiently than MRM or BEMD. The SFM produced sharp images of TB bacilli with better contrast. To further validate the results, multifractal analysis was performed that showed distinct variation before and after implementation of illumination correction by SFM. Results demonstrate that after illumination correction, there is a 26% increase in the number of bacilli, which aids in classification of the TB images into positive and negative, as TB positivity depends on the count of bacilli.

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

Evaluation of flow-volume spirometric test using neural network based prediction and principal component analysis.

In this work, an attempt has been made to enhance the diagnostic relevance of spirometric pulmonary function test using neural networks and Principal Component Analysis (PCA). For this study, flow-volume curves (N = 175) using spirometers were generated under standard recording protocol. A method based on neural network is used to predict the most significant parameter, FEV(1). Further, PCA is used to analyze the interdependency of the parameters in the measured and predicted datasets. Results show that the back propagation neural network is able to predict FEV(1) both in normal and abnormal cases. The variation in the magnitude and direction of parameters in the contribution of the principal components shows that FEV(1) is a significant discriminator of normal and abnormal datasets and is further confirmed by the percentage variance in the first few principal components. It appears that this method of prediction and principal component analysis on the measured and predicted datasets could be useful for spirometric pulmonary function test with incomplete data.

An approach to identify optic disc in human retinal images using ant colony optimization method.

In this work, an attempt has been made to identify optic disc in retinal images using digital image processing and optimization based edge detection algorithm. The edge detection was carried out using Ant Colony Optimization (ACO) technique with and without pre-processing and was correlated with morphological operations based method. The performance of the pre-processed ACO algorithm was analysed based on visual quality, computation time and its ability to preserve useful edges. The results demonstrate that the ACO method with pre-processing provides high visual quality output with better optic disc identification. Computation time taken for the process was also found to be less. This method preserves nearly 50% more edge pixel distribution when compared to morphological operations based method. In addition to improve optic disc identification, the proposed algorithm also distinctly differentiates between blood vessels and macula in the image. These studies appear to be clinically relevant because automated analyses of retinal images are important for ophthalmological interventions.

Abnormality detection in retinal images using ant colony optimization and artificial neural networks - biomed 2010.

Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented.

Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering.

In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Assessment and classification of mechanical strength components of human femur trabecular bone using texture analysis and neural network.

In this work the mechanical strength components of human femur trabecular bone are analyzed and classified using planar radiographic images and neural network. The mechanical strength regions such as Primary Compressive, Primary Tensile, Secondary Tensile and Ward Triangle in femur trabecular bone images (N = 100) are delineated by semi-automatic image processing procedure. First and higher order texture parameters and parameters such as apparent mineralization and total area associated with the strength regions are derived for normal and abnormal images. The statistically derived significant parameters corresponding to the primary strength regions are fed to the neural network for training and validation. The classifications are carried out using feed forward network that is trained with standard back propagation algorithm. Results demonstrate that the apparent mineralization of normal samples is always high as (71%) compared to abnormal samples (64%). Entropy shows a high value (7.3) for normal samples and variation between the mean intensity and apparent mineralization for the primary strength zone is statistically significant (p < 0.0005). The classified outputs are validated by sensitivity and specificity measurements and are found to be 66.66% and 80% respectively. Further it appears that it is possible to differentiate normal and abnormal samples from the conventional radiographic images. As trabecular architecture in the human femur is an important factor contributing to bone strength, the procedure adopted here could be a useful supplement to the clinical observations for bone loss and fracture risk.

Detection of obstructive respiratory abnormality using flow-volume spirometry and radial basis function neural networks.

In this work detection of pulmonary abnormalities carried out using flow-volume spirometer and Radial Basis Function Neural Network (RBFNN) is presented. The spirometric data were obtained from adult volunteers (N=100) with standard recording protocol. The pressure and resistance parameters were derived using the theoretical approximation of the activation function representing pressure-volume relationship of the lung. The pressure-time and resistance-expiration volume curves were obtained during maximum expiration. The derived values together with spirometric data were used for classification of normal and obstructive abnormality using RBFNN. The results revealed that the proposed method is useful for detecting the pulmonary functions into normal and obstructive conditions. RBFNN was found to be effective in differentiating the pulmonary data and it was confirmed by measuring accuracy, sensitivity, specificity and adjusted accuracy. As spirometry still remains central in the observations of pulmonary function abnormalities these studies seems to be clinically relevant.

Analysis on the erythrocyte shape changes using wavelet transforms.

The mechanics of red blood cell shape changes under normal and deformed conditions are analyzed using wavelet based approach. Images of intact and deformed human red blood cells obtained from normal adults are subject to morphological image processing and the corresponding shape descriptions at two different levels of approximations using different wavelet functions are analyzed. The results demonstrate that using wavelets it is possible to classify normal and deformed red blood cell shapes. Uniform and consistent results are obtained for cells with similar shapes, for all chosen wavelet functions. The variation indices are significant (p < 0.005) for all the chosen wavelet functions at both the approximation levels. Further it seems that this approach could be useful for identifying closely identical cell shapes. As cell shape deformations are significant in describing the flow behavior in micro or macro vessels the study seems to be clinically relevant. The methodologies, algorithms and observations based on wavelet based analysis are discussed in detail.