فصلنامه مهندسی پزشکی زیستی
سال نهم شماره 1 (بهار 1394)

Spinal deformities are generally associated with lumbar and cervical chronic pain and additionally they disturb the health. In these deformities, lumbar spinal curvature undergone changes in three dimensional space and in most cases, they cause reduction of lung capacities, breathing problems and negative effects on cardiovascular system. In critical deformity cases, in order to correct the deformity and prevent its progression, surgeons determine to perform posterior spinal fusion. As a result, they need to extract some important clinical parameters of spine such as Cobb angle, sagittal and coronal balance, spinal curvature, vertebraes angles and their rotations. In this study, edited tomographic images in MIMICS, were used to prepare a three dimensional model of the spine. Then by using curve fitting techniques and different clustering methods such as self-organization nueral network, k-means and hierarchical method, vertebras were separated and important geometrical data such as curvature of the spine and vertebras angle were obtained. In addition, through implementation of certain algorithms, other clinical features of each vertebra, including minimum and maximum height, length and width of the vertebral body and the relative displacement of vertebras were calculated automatically. In order to validate the proposed methods, measures and angles; derived values obtained automatically at each stage, were again calculated manually in MIMICS. Automatic values were verified by being compared with these manual results. In conclusion the reliability, accuracy and performance of the proposed automatic algorithms were demonstrated.

Patients with ACL deficiency (ACLD) have to use different compensatory mechanisms to maintain their stability during daily activities. The aim of this study is to determine the differences in 3D kinematics and peak ground reaction forces (GRF) between ACL deficient legs and healthy contralateral legs during stair ascent. Eight subjects with unilateral ACL deficiency participated in this study. Healthy contralateral legs were considered as control group for further comparisons to ACL deficient legs. A six camera VICON motion analysis system and 2 portable force plates were used to record the locomotion while walking up custom-made stairs with two different step heights. Advanced OSSCA technique was used to assess tibiofemoral knee kinematics, a combination of symmetrical axis of rotation (SARA), symmetrical center of rotation estimation (SCoRE) and optimal common shape technique (OCST). The results of this study show that participants with ACLD experienced different kinematics and peak GRFs in different step heights (p

In this paper the simulation of pathological behavior in gait locomotion of central nervous system (CNS) diseases and effects of rehabilitation techniques are investigated. These simulations noticeably deepen the knowledge of researches in neurorehabilitation realm about the neuroscientific basis of CNS treatment after a neural disorder. However, only a limited number of these simulations have been proposed in the previous works that issued some aspects of CNS diseases. Due to this limitation, in this paper, a more efficient simulation has been done on pathological behavior of neural disorders with including the brain signal disruption in the models. To do this, combinations of neural reflexes and central pattern generator (CPG) has been incorporated in the neuromuscular system and examined on two different msculoskeletal system containing two leg-one segment and one leg-two segment systems. Then, the locomotion of hemiplegia and paraplegia patients are simulated by inserting a malfunction in the supraspinal signal coming into the CPG. Moreover, the effects of rehabilitation effects on paraplegia patients have been investigated and compared to the experimental data.

Periodontal ligament (PDL) is a soft fibrous tissue which is located between tooth and alveolar bone. Because the tissue is softer than the surrounding tissue, tooth movement is forced to follow the movement of the soft tissue. The goal of this study is comparison of periodentium related to single and two root teeth behavior with applying different loads. The modeling of the real 3D geometry of incisor and premolar periodentium is carried out using micro CT scan. PDL is considered as hyperelastic material and stress-strain distribution was investigated by applying different loads. The results of finite element simulation show that tooth displacement with different loading is not necessarily in direction of loading and also stress distribution show that PDL absorbs the stresses, consequently alveolar carry less stresses. Strain distribution in PDL and alveolar bone stress represents uniform distribution of force in two root tooth. The analogy of results shows the accommodation with pervious studies.

Using methods based on nonlinear dynamics such as Poincare Section, can be useful in detecting dynamic biological systems. Selecting a suitable Poincare surface is a critical step in data analysis. Often finding an appropriate Position for Poincare Section needs to set different parameters. When the geometry of Poincare Surface picks the information related to the Stretching and folding, a better discrimination can be performed for the system states. The objective of this paper is to study the effect of position and degree of Poincare surface in Epileptic Seizure Detection. The Poincare surface resulting in the best classification is selected as the optimal section. Accordingly, the phase space of the EEG Segments Reconstructed in three dimension, firstly. Then, a set of Poincare surfaces with 400 different conditions of degree selected to cut the trajectory and Geometric features Extracted from the points of intersection on each surface. Afterward, extracted features from the Poincare section are applied to SVM classifier. Pearson correlation analysis was performed to analyze the relationship between the classification performance and degree of Poincare section. Certain behavior can be observed by increasing the Surface degree in output classifier. In this way, the increasing and then decreasing pattern were observed by increasing the Surface degree in two Directions of Surface. The results showed that the equation of optimal Poincare Section for m=12 and n=6 gives the accuracy of 96.6%.

Breast cancer is the most common type of cancer among women. The important key to treat the breast cancer is early detection of it because according to pathological studies 80% of all abnormalities are still benign at primary stages. Infra-red thermography is an imaging technique based on temperature distribution patterns of breast tissue. Compared with mammography, thermography is more suitable because it is noninvasive, non-contact, passive and free ionizing radiation. Here, a full automatic high accuracy technique for classification of suspicious areas in thermogram images with the aim of assisting physicians in early detection of breast cancer presented. Proposed algorithm consists of four main steps: pre-processing & segmentation, feature extraction, feature selection and classification. At the first step, using full automatic operation, region of interest (ROI) determined and the quality of image improved. Using thresholding and edge detection techniques, both right and left breasts separated from each other. Then relative suspected areas become segmented and image matrix normalized due to the uniqueness of each person's body temperature. At feature extraction stage, 23 features, including statistical, morphological, frequency domain, histogram and Gray Level Co-occurrence Matrix (GLCM) based features are extracted from segmented right and left breast obtained from step 1. To achieve the best features, feature selection methods such as mRMR, SFS, SBS, SFFS, SFBS and GA have been used at step 3. Finally to classify and TH labeling procedures, different classifiers such as AdaBoost, SVM, kNN, NB and PNN are assessed to find the best suitable one. The results obtained on native database showed the best and significant performance of the proposed algorithm in comprise to the similar studies. mRMR combined with AdaBoost with the maximum accuracy of 92%, and SFFS combined with AdaBoost with a maximum accuracy of 88%, are the best combination of feature selection and classifier.

In this paper, a transparent bilateral controller is developed for the control of telesurgery systems that have physical interactions with soft tissue. In this control method, the parameters of a viscoelastic model of the soft tissue are estimated during its interaction with the slave robot using an on-line identification method. These estimated parameters are used in an impedance control of the master robot which is in contact with the surgeon. Also, the slave robot tracks the master robot position using a tracking controller. Accordingly, it is shown that the transparency of the teleoperation system is obtained by estimating and realizing the dynamic parameters of the tissue for the master robot and providing the position tracking performance for the slave robot. The stability, and the position and force tracking performances are proved using the Lyapunov theorem. Moreover, the effectiveness of the proposed transparent bilateral controller is investigated by simulations performed on a piece of beef (as the soft tissue) using a two DOF robot with nonlinear dynamics. The proposed control strategy can be used in telesurgery, telesonography and telerehabilitation systems in which the robot interacts with soft tissues.

Although the cognitive deficits due to age-related brain differences have been largely analyzed, the altered connectivity of task related functional networks in aging requires more studies. As the brain of old adults experience some alterations in task performance during cognitive challenges, the related effects on connectivity of functional networks are here evaluated using event-related functional Magnetic Resonance Imaging (fMRI). The fMRI data have been acquired for simple visual and motor tasks from 18 subjects (8 young and 10 elderly). For each subject, several Functional Connectivity (FC) networks including, motor, visual and the default mode networks are firstly calculated using a conventional voxel-wise correlation analysis with predefined region of interest. Then, the strength of functional connectivity is assessed and compared for different age groups. The current study has evaluated three hypotheses on FC of aging brain: the frontal regions involved with motor network try to compensate for declines in the posterior regions, default-mode network are less suppressed and, the posterior regions involved with visual network exhibit less connectivity. The first two hypotheses are accepted by analysis results but visual network behaves differently. Also, results show that the task related functional connectivity is considerably altered in old adults compared to young adults. Old adults demonstrate higher connectivity strength on average with a slightly smaller variance than young adults.