Journal of Mechanics in Medicine and Biology

The existing functional electrical stimulation (FES) techniques often required to solve the complex "inverse dynamic problem" to calculate the muscle torques for moving along a desired trajectory. According to the threshold control theory of voluntary motor control, a bio-mimetic threshold control strategy for the FES controller is designed and tested in the human arm movement. The arm is modeled as three segments connected by two hinges joints. The movement is driven by seven muscles and limited in the horizontal plane. All muscles are described by a modified Hill-type muscle model. Simulation results suggest that the threshold FES control system can realize point to point movement and can approximately follow the desired traces in presence of feedback delays up to 20 ms. The movement can also maintain stability under external perturbation or external load. The control system can be employed in clinical application because of the following advantages: (1) The control strategy includes some mature control techniques which had been realized in hardware. (2) Only sophisticated sensors of goniometer and the surface electrodes are needed to provide feedbacks and muscle stimulation. (3) The performance of the control system will not be critically influenced by the slight change of musculo-tendon parameters and feedback delays, and even the parameters of controller are fixed.

Objective. The aim of this paper is to show an algorithm for the automatic computer-based tracing (ACT) of common carotid artery (CCA) in longitudinal B-mode ultrasound images characterized by four main features: (i) user-independence; (ii) suitability to normal and pathological images; (iii) robustness to noise; and (iv) independent of ultrasound OEM scanner.

Methods. Three hundred longitudinal B-mode images (100 normal CCAs, 100 CCAs with increased intima-media thickness, 60 stable plaques, and 40 echolucent plaques) were acquired using three different (GE, Siemens, and Biosound) OEM ultrasound image scanners. The algorithm processed each image to delineate the region of interest containing the CCA. Output of the algorithm are three segmentation lines representing (a) distal (far) and (b) near adventitia layers, and (c) lumen of the CCA. Three operators qualitatively scored the ACTs.

Results. The CCA was correctly automatically traced in all the 300 B-mode images. The performance was independent on the image scanner used to acquire the image or on the type of the CCA (healthy versus pathologic). Eight ACTs out of 300 received a poor score after visual inspection due to an automated adventitia tracing that did not correctly follow the CCA wall in a small portion of the image.

Conclusions. The proposed algorithm is robust in ACTs of CCA since it is independent of scanner and normal/abnormal wall. This approach could constitute a general basis for a completely automated segmentation procedure.

Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.

Esophagitis is essentially inflammation of the esophageal squamous mucosa. One of the major reasons for cause of Esophagitis is the acid reflux from the stomach. This condition is observed in the process of upper gastro-intestinal tract endoscopy and the diagnosis is arrived at by examining the images of the esophagus. The diagnosis is based on the observation of the lesions and coloration of the digestive mucosa. Our paper reports an implementation of Decision Support System (DSS) for diagnosis of Esophagitis based on the analysis of color and texture features of the images captured during the process of endoscopy. The Hue Saturation and Intensity color model is adapted. The statistical features of the Hue and Saturation form the color features and the texture features are determined by Discrete Cosine Transform coefficients of the image. The decision making structure is a feed forward neural network. The DSS has been tested and results are reported.

Epilepsy is a brain disorder causing people to have recurring seizures. Electroencephalogram (EEG) is the electrical activity of the brain signals that can be used to diagnose the epilepsy. The EEG signal is highly nonlinear and nonstationary in nature and may contain indicators of current disease, or warnings about impending diseases. The chaotic measures like correlation dimension (CD), Hurst exponent (H), and approximate entropy (ApEn) can be used to characterize the signal. These features extracted can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions. These nonlinear features have been reported to be a promising approach to differentiate among normal, pre-ictal (background), and epileptic EEG signals. In this work, these features were used to train both Gaussian mixture model (GMM) and support vector machine (SVM) classifiers. The performance of the two classifiers were evaluated using the receiver operating characteristics (ROC) curves. Our results show that the GMM classifier performed better with average classification efficiency of 95%, sensitivity and specificity of 92.22% and 100%, respectively.

Left ventricular (LV) contraction is the basis of LV systolic function, impairment of which underlies heart failure pathophysiology. Its accurate quantification in the form of LV contractility indices is imperative for diagnostic and follow-up assessment of LV systolic function in heart failure.

Herein, we analyze LV contractile performance by focusing on LV contractility indices at different physiological organizational levels: from sarcomere dynamics to LV myocardial properties (such as elastic modulus and elastance), and from LV wall contractile stress development to the generation of intra-LV blood flow velocities and pressure distributions. Further, we present the development analyses of these indices and their medical applications. Using improved development of invasive and noninvasive techniques for measuring ventricular pressure, geometry, and volume, we show how these indices have become more amenable for clinical usage to obtain better patient assessment.

The purpose of this paper is to present a comprehensive coverage of LV contraction physiology, indices to qualify LV contraction, formulation, and medical applications of some major intrinsic LV contractility indices, so as to provide the basis of functional assessment of normal versus diseased hearts.

At present, percutaneous liver biopsy is the gold standard in assessing liver fibrosis such as hepatitis and cirrhosis, but there could be sampling error, and specimens might not represent the state of the whole liver accurately because only about 0.002% of the organ is sampled. In this research, we propose the three-dimensional fiber structure extraction echo filter to realize a quantitative ultrasonic diagnosis. The filter is designed based on a statistical theory, and it is possible to reduce the noise contained in a back scattered ultrasonic echo signal, and to visualize the structure of a fiber.

The eyes are complex sensory organs, they are designed to capture images under varying light conditions. Eye disorders, such as cataract, among the elderly are a major health problem. Cataract is a painless clouding of the eye lens which develops over a long period of time. During this time, the eyesight gradually worsens. It can eventually lead to blindness and, is common in older people. In fact, about a third of people over 65 have cataracts in one or both eyes.

In this paper, we made use of two types of classifiers for identification of normal, cataract (early and developed stage), and post-cataract eyes using features extracted from optical images. These classifiers are artificial neural network and support vector machine. A database of 174 subjects, using the cross-validation strategy, is used to test the effectiveness of both classifiers. We demonstrate a sensitivity of more than 90% for both of these classifiers. Furthermore, they have a specificity of 100% and, as such, the results obtained are very promising. The proposed feature extraction and classification systems are ready clinically to run on a large amount of data sets.

We report a novel method of reconstructing the 3D structure of the prostatic urethra and measuring its elasticity using endoscopic video images, and discuss their relation to clinical relevancy. Information regarding pixel color and brightness in the endoscopic video image is converted to relative distance between the object and the light source. An opened, 3D image of the prostatic urethra is obtained from a video image captured by the endoscope as it is slowly pulled through the urethra. The elasticity of the urethra is determined by recording a video image of the endoscope fixed in the prostatic urethra, with and without irrigation under water pressure of approximately 80 cm H₂O. Angulation of the prostatic urethra is estimated by the number of intersections between the outline of protruded prostate and the midline of the urethra in patients with severe voiding dysfunction scheduled for transurethral resection of prostate, and in those scheduled for transurethral resection of bladder tumor without apparent discomfort during urination. The number of intersections showed a relationship with voiding symptoms. In conclusion, reconstruction of the 3D structure of the prostatic urethra from endoscopic video images is a feasible method that shows promise for estimating the mechanism of voiding dysfunction.

This paper presents a new technique to assess the bone formation after periapical dental surgery. The proposed technique consists of two main stages: image registration and spectral subtraction stages. Image registration is used to avoid projection errors produced due to nonstandardization of X-ray scanners. Wavelet coefficients are used instead of grey values for registering the images. Coarse to fine strategy with four levels of resolutions is used to speed up the process. The second stage is the spectral subtraction stage. It is used to yield the difference image between pre- and post-operative images which represents the bone gain or bone loss with light and dark areas, respectively. Algorithm has been applied on a number of pre- and post-surgery intra oral periapical (IOP) dental X-ray images. Mean and root mean square error (RMSE) are computed to assess the quality of registration technique. The technique presented here is compared with grey level based method; results show that proposed technique outperforms conventional grey level method based on dyadic sampling.

Automated analysis and interpretation of retinal images has become an incontournable diagnostic step in ophthalmology. Retinal blood vessels morphology can be an important indicator for diseases such as diabetic retinopathy; and their detection also serves for image registration. This paper presents a method based on mathematical morphology for extraction of vascular tree in color retinal image with low contrast. It consists in contrast enhancement and application of watershed transformation in order to segment blood vessels in digital fundus images.

In medicine, for making a diagnosis, many tests are needed. It may so happen that some test results may be in the normal range, while some test results may be abnormal. So how is the doctor going to precisely decide how "sick" is the patient: is s/he at risk, or marginal, or very sick?

Hence, we have developed the new concept of a Nondimensional Physiological Index (NDPI). This NDPI is made up of a number of parameters characterizing organ function and dysfunction or a physiological system function and disorder or an anatomical structure's property and pathology, in the format of a medical assessment test; the NDPI combines these parameters into one nondimensional number. Thus, the NDPI enables the doctor to integrate all the parameters' values from the medical test into one nondimensional index value or number. Then, by examining a large number of patients, we can determine the statistical distribution of that particular NDPI into normal and abnormal categories. This makes it convenient for the doctor to make the medical assessment or diagnosis.

Now for an organ or physiological system assessment test (such as a Treadmill test or Glucose tolerance test) or for an anatomical structure's property and pathology determination (such as for determining mitral valve calcification and pathology), the method of formulating and evaluating the NDPI (from the medical test) entails developing its bioengineering model equation incorporating the parameters characterizing the organ state or physiological system function or the anatomical structural constitutive property. These parameters are adroitly combined into a NDPI, so that the NDPI unambiguously conveys the normal and abnormal state of the organ or physiological system or anatomical structure. This bioengineering model's governing equation or solution (involving the model parameters) is then applied to fit or simulate the monitored test data of the physiological system or the anatomical structure. The model parameters are then evaluated (from the simulated solution to the test data), and their ranges are determined for normal and abnormal states of the organ or physiological system or anatomical structure. Then, the NDPI (composed of the parameters of the organ function or physiological system function or the anatomical structural constitutive property) is also evaluated for normal and abnormal states of the patient's organ or physiological system or anatomical structure.

In this way, we can apply these NDPIs to reliably diagnose the patient's state of health, from preferably noninvasive medical assessment tests. In this paper, we have developed a number of noninvasive medical tests involving NDPIs, based on biomedical engineering formulations of organ function, physiological system functional performance and anatomical structural constitutive property, to provide the means for reliable medical assessment and diagnosis. These tests include (i) some conventional tests, such as Treadmill and Glucose tolerance tests, (ii) determination of cardiac contractility (and myocardial infarcted segments) and lung ventilation performance characteristics (and lung diseases) as well as (iii) some of our newly formulated tests, to detect arteriosclerosis, aortic pathology, mitral valve calcification, and osteoporosis.