IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING
Year:2019 | Volume:12 | Issue:4 |Papers Count:7

Matching of the protein spots in two dimensional gel electrophoresis (2DGE) images is a main process of analyzing these images. Due to the challenges of 2DGE images such as the presence of noise and artifacts, the matching of protein spots is performed under human supervision. This supervision involves human errors. Therefore, in this work a new automated algorithm has been proposed for spot matching in 2DGE images which is based on a probabilistic model. Due to the complexities of the proposed model, the Variational Bayes has been used to solve the equations of the model. The performance of the proposed algorithm has been evaluated on real and synthetic 2DGE images with some statistical criteria. Protein spots in real image dataset have been matched by the proposed method with angular error of 0. 05 and end point error of 1. 46 and in synthetic image dataset with angular error of 0. 13 and end point error of 0. 90. These results reveal higher precision and effectiveness and lower matching error of the proposed method.

Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works to this subject and many algorithms have been proposed. In this paper we present an automatic spike detection algorithm for the noise-contaminated extracellular signal. This algorithm consists of four steps: 1-A bandpass filtering and using a differential filter; 2-applying Shannon's energy nonlinear filter; 3-Hilbert transform; and 4-Thresholding of the signal. The proposed method has been compared with five known methods in spike detection. This comparison is done on two simulated datasets and one real data set. The results indicate the superiority of the proposed method for simulated data compared to other methods, which indicates the robustness of the proposed algorithm to the noise. Meanwhile, for real data, it reaches the second place among all six methods. Using Shannon's non-linear energy filter can be an effective way to detect spikes in extracellular signal recordings. The comparison indicates that this method is superior to the commonly known methods for spike detection.

Mock circulation systems are necessary for in vitro experiments and development of the ventricular assist devices, heart valves, total artificial hearts, artificial lungs, vascular grafts and intra-aortic balloon pumps. Tissue engineering of heart valves and other preliminary studies on the cardiovascular system can also achieve with mock circulation systems. One of the major parts of these systems is generating of pulsatile flows like heart. In this study a mock circulation system with ability of physiological pressure and flow simulation in both healthy and diseases heart has been designed and manufactured. This setup can produce pressure and flow pulse by using a positive displacement pump with programing of movement pattern of stepper motor. This pump was manufactured after designing by Solid-works software for using in the mock circulation system. For testing this setup, several experiments with different values of peripheral resistance and arterial compliance and changing the pattern of the stepper motor have been performed. The results show modifying the pattern of the stepper motor from uniform speed to variable speed in one cycle (360 degree), as well as changing in arterial compliance and peripheral resistance, can produce waveform of physiological pressure and flow. The results confirm the reliable performance of this system in simulating physiological conditions. The designed system has the software capability to generate different waveforms.

During vision process, the information produced by rod and cone photoreceptors is compressed in retina and then is transmitted by three separated pathways of ganglion cells, Magno, Parvo and Konio, to the upper level processing centers. There are electrophysiological and psychophysical evidences that these three pathways show characteristic patterns of malfunction in multiple sclerosis (MS) patients. Although fMRI can provide accurate localization of the neural activities in these pathways, there is no fMRI study on malfunctions of these pathwyas in MS yet. So by employing the differences in structure and function of these cells, we generated three different visual stimuli with different spatial and temporal frequencies to stimulate each pathway separately. These stimuli were shown to the subject inside MRI scanner by a calibrated projector located outside of scanner room. The fMRI data were acquired from two groups of normal and MS subjects (each including 5 subjects) by using a standard protocol. Finally, the activation results in visual lobe and LGN were analyzed in within-group and between-group levels. The group analysis of fMRI data was performed by using general linear modeling (GLM) and fixed-effect method via FSL software and results showed patterns of malfunctions in visual cortex and LGN in MS group. Also, among Magno, Parvo, and Konio cellular pathways in LGN, just the activation of Magno cellular pathway showed significant malfunction in MS group.

Wireless capsule endoscopy is a non-invasive diagnosis method which allows recording a video as the capsule travels through the gastrointestinal tract. The practical drawback is producing a long clinical video up to 8 hours and it takes about 2 hours to review the exam by an experienced expert. Video summarization methods can reduce the time required by experts and errors in manual interpretation. This paper presents an automatic method based on unique properties of adaptive singular value decomposition through sliding window that can reduce the long annotation time. By utilizing these properties, we are able to summarize a WCE video by outputting a motion video summary. Moreover, we apply an effective approach based on adaptive contrast diffusion to correct uneven illumination that deal with the low contrast generally caused by poor visibility conditions of the GI tract, WCE power and its structure. Experimental results on WCE videos indicate that a significant reduction in the review time is feasible. Quantitative and qualitative results of summarization show the effectiveness of proposed method that can be adapted to various clinical applications, such as training of young physicians, computer assisted diagnosis, medical decision support or medical document management.

Balance is essential for human daily activities. Standing on an unstable platform requires continuous effort of the neuro-musculoskeletal system. Cognitive interference and support surface perturbation may cause loss of balance. The aim of this study is to evaluate the ability of stability provision of individuals while standing in different levels of postural and cognitive difficulty. To this end, twelve healthy young women were participated in six levels (three levels of support surface × two levels of cognitive intereference). Three levels support surface were standing on a firm surface, unstable platform surface with and without spring support. Two levels of attentional cognitive involvements were considered with or without questions by presenting on a curtain and asking to response by a yes/no joystick. Motion analysis was used to measure joint angles by capturing body movements in the sagittal plane by a high-speed camera and active markers. To quantitatively investigate the stability, two linear (pathlength, root mean square) and two nonlinear (approximate entropy, fractal dimension) metrtics were calculated. Results showed that the ankle mechanism plays a more prominent role in keeping balance than the knee and hip joint mechanisms. Merely the approximate entropy indicated significant differences between the postural difficulty levels. Also, the mediocre level of support surface perturbation (spring-supported unstable platform) revealed multi-joint collaboration between the mechanisms. The inconsistence between postural and cognitive difficulty levels might vanish the role of cognitive questions in the present study. Therefore, considering consistent postural and cognitive tasks may highlight the effects of cognitive involvements on standing.

Prostate cancer is one of the most important diseases of men whose growth can be disrupted by early diagnosis of it. In order to determine the grade of prostate cancer, the biopsy is used and structure of tissue is examined under microscopes. According to new grading system, the prostate tissues are grading to five categories, between 1 to 5, where the highest grade shows the worst condition. Since human grading is time consuming, automatic grading systems have been used since recent years. Although some efficient algorithms have been introduced for image classification, the semantic gap between low-level features and human visual concept is still an important reason not to achieve high precision. In this paper, a new method for prostate cancer grading is presented which uses a combination of deep features, extracted by convolional neural network (CNN), and stochastic tissue features, extracted using multi-level gray level co-occurrence matrixes (ML-GLCM). Therefore, high-level features are achieved by using CNN and by combining with stochastic tissue features, the grading precision is increased. In order to evaluate the proposed method, it is examined on the pathology prostate image database which is generated by international society of urological pathology (ISUP). Experimental results demonstrate that the proposed method achieves more accuracy than state-of-the-art methods on prostate cancer grading.