جواد حدادنیا

In modern prostheses, accurate processing of surface electromyogram (sEMG) signals has a significant effect on optimal muscle control. Although these signals are useful for diagnosing neuromuscular diseases, controlling prosthetic devices and detecting hand movements, non-robustness of EMG signal-based recognition will give rise to various movement disorders. In this paper, we present an optimal approach to classify EMG signals for hand gesture and movement recognition, whose purpose is to be used as an efficient method of diagnosing neuromuscular diseases, determining the type of treatment and physiotherapy. The main assumption of this study is to improve the accuracy of recognition and therefore, we proposed a novel hand gesture and movement recognition model consists of three steps: (1) EMG signal features extraction based on time-frequency domain and fractal dimension features; (2) feature selection by soft ensembling of three procedures in which includes two sample T-tests, entropy and common wrapper feature reduction, and (3) classification based on kernel parameters optimization of SVM classifier by using Gases Brownian Motion Optimization (GBMO) algorithm. Two UC2018 DualMyo and UCI datasets have been considered to evaluate the proposed model. The first dataset is used to classify eight hand gestures and the second dataset is employed for the classification of six types of movement. The experiment results and statistical tests reveal that the designed approach has desirable performance with an average accuracy of above 98% in both datasets. Contrary to similar methods that perform classifications in finite classes with high error rates, the integrated method has satisfactory accuracy, robustness and reliability. Not only the proposed method contributes to the design of prostheses, but also provides effective outcomes for rehabilitation applications and clinical diagnosis processes.

Breast cancer is the most common cancer in women. Accurate classification of breast cancer has a key role in medical diagnosis. Hence, researchers seek optimized methods to improve tumor diagnosis.
The current study presents bio-geographical based optimization neural network for classifying data as benign and malignant using principal component analysis in preprocessing stage and updating weights concurrently. The presented algorithm was assessed using the data from Wisconsin databank.
Classification accuracy in a normal state, that is, without applying principal component analysis and an optimization algorithm, and applying only neural network at a ratio of %70 to %30 from training and testing set is %97.2. Accuracy reaches %98.5 after applying principal component analysis and decreasing features from nine to eight. Finally, using bio-geographical based optimization algorithm with a 10-fold cross validation, accuracy reaches %100, which is significantly more successful than other similar studies.
Applying this algorithm can optimize the performance of the neural network. The optimal performance of this method is revealed by comparing the proposed method with the non-optimized method and the approach which used only PCA and neural network method. The results suggest that the method presented in this paper had a high accuracy in classifying breast cancer data and can be used for its diagnosis.

Mammography is the most common modality for screening breast cancer. In this paper a computer aided system is introduced to diagnose benignity and malignancy of masses.
In the first step of the proposed method, masses are prepared for segmentation using a noise reduction and contrast enhancement technique. Afterwards, a region of interest is segmented using a new adaptive region growing algorithm, and boundary and texture features are extracted to form its feature vector. Consequently, a new robust architecture is proposed to combine weak and strong classifiers to classify masses. Finally, the proposed mass diagnosis system was also tested on mini-MIAS and DDSM databases.
The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy.
The novelties of the proposed system can be summarized as presenting a new automatic adaptive region growing algorithm to extract boundary of masses, using descriptors based on empirical mode functions, and introducing a new framework for combing classifiers.

Breast cancer is the most common cancer in women and one of the leading of death among them. The high and increasing incidence of the disease and its difficult treatment specifically in advanced stages, imposes hard situations for different countries’ health systems. Body temperature is a natural criteria for the diagnosis of diseases. In recent decades extensive research has been conducted to increase the use of thermal cameras and obtain a close relationship between heat and temperature of the skin's physiology. Thermal imaging (thermography) applies infrared method which is fast, non-invasive, non-contact and flexibile to monitor the temperature of the human body. This paper investigates highly diversified studies implemented before and after the year 2000. And it emphasizes mostly on the newely published articles including: performance and evaluation of thermal imaging, the various aspects of imaging as well as The available technology in this field and its disadvantages in the diagnosis of breast cancer. Thermal imaging has been adopted by researchers in the fields of medicine and biomedical engineering for the diagnosis of breast cancer. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. Thermography does not provide information on the structures of the breast morphology, but it provides performance information of temperature and breast tissue vessels. It is assumed that the functional changes occured before the start of the structural changes which is the result of disease or cancer. These days, thermal imaging method has not been established as an applicative method for screening or diagnosing purposes in academic centers. But there are different centers that adopt this method for the diognosis and examining purposes. Thermal imaging is an effective method which is highly facilitative for breast cancer screening (due to the low cost and without harms), also, its impact will increase by combining other methods such as a mammogram and sonography. However, it has not been widely recognizesd as an accepted method for determineing the types of tumors (benign and malignant) and diseases of breast tissue.

Malignant melanoma is one of the most dangerous types of skin cancer. The aim of this study was to evaluate Spatial Thermographic Imaging (STI) in differentiating benign from malignant skin lesions.
One-hundred and ten STI images were taken from the volunteers who had lesions being suspected for malignant melanoma. Benignity or malignancy of lesions was determined according to the changes detected in tissue using texture filters. The STI data were compared with histopathology results.
Out of 110 investigated patients with STI, three cases of malignant melanoma were found. Two cases were in men and the other one was in a woman. Accuracy of STI in diagnosis of malignant melanoma was 98%.
STI may potentially be used as a non-invasive method for the detecting skin malignant melanoma.

In the process of telemedicine, the use of digital techniques for the diagnosis of diseases has made physicians to use ring resources and high bandwidth in data transfer for archiving and storing patient information. The aim of this paper is to introduce an efficient way of multi-stage compression of mammographic image data based LM algorithm and artificial neural networks. First, the data derived from mammographic images are given to multi-layer neural network and the proposed structure allows the sample images to be formed with the minimum damage and the degree of compaction in the first layer. The compression process of the mammography images was implemented using images of 128 women aged 46.41±6.55 with a BMI of 36.78 ±5.5 collected from three specialized clinics in Sabzevar. The analysis yielded a mean square error (MSE) of 4.24 with the highest difference ratio of 33.46 and compression ratio of 8: 1in the output of the algorithm. The system performance was acceptable based on the accurate design of the software, and therefore it demonstrated high efficiency in practice. Given the reliability of software output in the compression and release and the fact that mammographic images are not completely degraded during compression, the diagnosis in the discovery stage is highly consistent with the diagnosis in the reality. Therefore, this system has the capacity to be implemented in hospitals for archiving mammography images and its application is justifiable.

Diabetes is such diseases that need high quality beside prevention such as correctly predict fluctuations in blood glucose levels. The main complications of the disease can be anesthesia, coma and even death. Today, in these patients, the correct dose of insulin determined based on experience or doctors knowledge, and interact between the patients and physician, although there is an inevitable human errors. In this study based on applied method, 124 patients and 188 healthy subjects based on 12 features by Random Selection, Who had been referred to Research Center for Diabetic in Sabzevar university of Medical Science since 2006 to 2011 were studied. The proposed system has several subsystems, such as evolutionary algorithms (BPS 1) to select the most effective features, Data Mining Algorithms (SVM 2) to detect and classify the features from the non-effective features. Adaptive Neuro fuzzy systems (ANFIS 3) to estimate learn and adaptation in order to correctly predict have been used. In this study, we try to use artificial intelligence systems to determine the correct dose of insulin for diabetics. The proposed system combines the best attributes in the database in the form the interaction was able to achieve high accuracy with the lowest error. The proposed system based on best features in the database in the interaction form was able to achieve high accuracy with the lowest error. The proposed system in the form of composition and interaction with the subsystem was able to achieve carefully 84.1% in specificity, 91% in sensitivity and 92.9% in accuracy. In this research, due to the importance of correct and timely determination of insulin for diabetics, a new method based on the combination of intelligent systems is presented. Thus, the results obtained in previous articles and studies provide significantly improved.

Currently، there are many techniques to measure subcutaneous body fat but these methods have many limitations. In this study، we tried to provide a clustering algorithm to measure the thickness of subcutaneous fat in thermography images. For the detection of subcutaneous adipose tissue in the midline area (from pubis to the xiphoid process)، imaging takes place in the right or left lateral sides of the concerned person and to detect this tissue at the left and right flank (from ribs to the iliac crest)، imaging takes place from the front. This study was done on 100 subjects (50 female، 50 male) of patients referred to the Shahid Mobini Hospital of Sabzevar since April 2013/4 to December، 2013 and the thickness of their subcutaneous fat in midline abdomen from pubis to the xiphoid process and flank from ribs to the iliac crest were measured based on thermal model and using K-Means and Fuzzy c-means (FCM) clustering methods and also recursive connected components algorithm. Subcutaneous fat tissue can quickly appear in the thermogram as an area of low temperature and since in the thermal images، temperature is characterized by the color، as a result، subcutaneous fat tissue must have lower levels of color (temperature) relative to internal body tissues. All the measurements based-on thermal images to determine the maximum thickness of subcutaneous fat were compared with ultrasound. The results of our method were similar to the results of ultrasound method done by a radiologist، with the acceptable approximation. The method presented in this paper is considered as a noninvasive and cost-effective method to measure the thickness of subcutaneous body fat.

Seizures can be noted to the main symptom of epilepsy. Seizures prediction or early diagnosis for people reduces significantly injuries of epilepsy. The main problem that related to neurological disorders is an inability to timely prediction or the occurrence of seizures. EEG signals are Stochastic Process that can be treated as a sequence in time or in other words can be stated time series. In this study 300 epileptic patients categorized in three groups: normal, before and during the convulsive seizures were studied. Accordingly, after receiving data, they were preprocessed, then for Prediction time occurrence extracted special features by propose Algorithm. Eventually In order to final validate the cross-evaluation method (k-fold) has been used. Firstly by wavelet transforms (WT), removed possible artifacts. In the next step by Binary Particle Swarm Optimization (BPSO) the characteristics (delay) are obtained. Then SVM algorithm (SVM) was performed to dimension reduction and manage the data (delay) so final Prediction that applied by Adaptive Nero Fuzzy Inference System Based on Optimal Delay. The final evaluation and final validation were done and the algorithm accurately in predicts by 2 units in delay approved. The Proposed System achieved a high accurate by interaction in introduced method. Despite the high accuracy, the present methods have a little ability in predicting seizure. Comparing the current methods indicate accuracy and high efficiency of the present approach.

Diabetic foot ulcers are one of the most common complications of diabetes that affect more than 15 percent of diabetic people. In the other hand، due to problems in the immune system of diabetic patients، these ulcers are often slow to take hold and this dramatically increase the risk of gangrene، amputation and even death. Conventional methods to identify vulnerable areas to ulcers that are based on the examination and palpation of the skin، this usually can’t reveal changes of integrity of the skin at the right time to prevent the ulcers. Therefore in recent years، thermal imaging from the body as a new method to early diagnosis has been considered to be as a ulcers area. This method is based on comparing the left and right feet‘s temperature to identify areas of inflammation occurs. In this study from 25 participants feet’s thermal images were taken in the Image Processing Laboratory، University of Hakim Sabzevari at standard conditions and Then The algorithm is fully automatic order to compare the thermal image accurately and extraction of passionate area from the temperature matrix for correspondent comparison of left and right feet‘s temperature Is designed to identify areas of hidden inflammation. The proposed system is designed based on image processing and computer vision techniques، Able to find two cases of inflammation were among the participants in the study. The accuracy، sensitivity and specificity of the algorithm، respectively equal of 90%، 100% and % 88 achieved. Unlike past researches that is more for the quality of their action on the thermal images، we managed to design quite a smart system that quantitatively comparing peer to peer parts of the left and right people’ planetary، in order to identify the inflammation area. While the researchers said that when a patient does not feel the monofilament testing and couldn’t noticed the temperature of his/her soles of his/her feet، In fact، the only method to prediction and prevention of creating ulcers in a foot without sense، is detecting the temperature difference، that designed algorithm in this research well and accurately، makes meet The authenticity and acceptability of this demands.

Skin cancer is the most common type of cancer constitutes and 75% of all cancers in the world. Malignant melanoma is the most invasive and lethal form of skin cancer. The objective of this study was to evaluate infrared thermography in differentiation of benign and malignant skin lesions. This study was conducted on 120 patients (60 female، 60 male) with a lesion suspicious for melanoma and identifying the type of lesion (benign or malignant) based on thermal model using Fuzzy C-Means (FCM) clustering. In this imaging method، the difference on temperature responses between the normal and abnormal tissue was compared and data obtained was compared with biopsy results. In 120 cases studied، 3 cases of skin cancer (2 male، 1 female) were found which all could be detected based on analysis of thermal images. At present، new methods for the detection of skin cancer، especially non-invasive diagnostic tools، have been discovered in several laboratories. The method presented in this paper could be considered as a non-invasive and cost effective method for the detection of skin cancer.

Human body temperature can be a natural index in diagnosing some diseases. Thermal imaging (Thermography), which works on the basis of infrared radiations, is a fast, non-invasive, non-contact and flexible method for monitoring human body temperature. Additionally, thermal imaging can map the body surface temperature in a non-contact manner. In recent years, extensive research has been conducted with the aim of increasing the use of thermal cameras and obtaining a close relationship between skin temperature and thermal physiology. Thermal imaging has been successful in diagnosing breast cancer, diseases related to blood pathology (e.g., arteriosclerosis, peripheral vascular disease, deep vein thrombosis, effects of vasoconstrictors such as nicotine) - diseases related to joint and bone pathology, and especially in tumoral pathology and pediatrics diseases. This paper investigates the function of thermal cameras, and capabilities and advantages of thermal imaging method in the diagnosis of various diseases.

To develop a fast algorithm for automatic accurate extraction of blood vessels from retinal images in order to facilitate image analysis and minimize the rate of diagnosis error. We proposed an efficient algorithm using adaptive filter and thresholding technique which are based on fuzzy entropy. The data included retinal images from 60 index normals and patients (including 38 men and 22 women) obtained from STARE and DRIVE databases. The mean subject age was 47.6±9.1 years. In images from index patients, the presence of abnormal tissue was confirmed by two specialists. Initially, image blurring caused by vibrations during image capturing was eliminated using designed Wiener filter. The impact of optical reflection in each image was minimized through transferring images into the HSV space and selecting the spatial mask for retina. Extraction of retinal blood vessels was done using adaptive filtering. Finally, to separate the main artery of the retina from the rest of the tissues, the optimal threshold was obtained which was defined by the fuzzy entropy technique. By using combined methods in pre-processing step of retinal images, the optic disc border and radiant reflections of ophthalmoscope disappeared in the output image. Therefore, the accuracy (0.9475±0.08) and F-measure (0.7426±0.05) increased significantly. Fast and accurate extraction of blood vessels in the retinal images enables the ophthalmologist to predict disease progression, hence suggesting appropriate treatment modality.

The aim of this study was to investigate the effects of two types of quran voice، Majlisi or Tartil on IgA، IgM، IgG، C3، C4 and White Blood Cell count. Methods and Materials: this study was conducted on 60 volunteer students in sabzevar university of Medical sciences. Subjects were divided randomly into 2 groups. In the first group، participants heard to Majlisi voice and in the second group، subjects heard to Tartil voice of Quran for 20 minutes. Blood sampling were done before and after Quran hearing. IgA، IgM، IgG، C3 and C4 were measured by SRID and kit method and white blood cell count was done by cell counter. Data were analyzed using SPSS16 software and U Mann – Whitney and Kruskal Wallis tests. Hearing the Majlisi voice significantly increased the IgG (p=0. 032)، C3 (p=0. 049) and C4 (p=0. 004)، but it did not change other factors. Hearing the Tartil voice rose significantly gust the IgG (p=0. 008) and C3 (p=0. 001). The results of this study shows that hearing to both types of Quran voice (Majlisi or Tartil) can go up the immune system factors and inhance this to encountering some stressful and critical situations.

Many countries are faced with the growing population of the elderly each year and so designing an appropriate system for monitoring of various elderly states is necessity. Every year thousands of the elderly suffer serious damages such as articular fractures، broken bones and even death due to their fall Methods & Materials: In this paper، based on the analysis of images taken from the elderly’s movement، an efficient system has been proposed that، in the first phase، simulates the movement of the elderly by detecting their abnormal walking. The، by combining several important features، including an estimate of body angle، representation of the motion and estimate of the magnitude and direction of movement، the speed of the falling is calculated. This system has been implemented on a set of 57،425 video frames received from the elderly residing in Farzanegan Health Care Center in Mashhad and the video sequences containing the actual occurrence the of falling. All the sequences were randomly converted into four Movie categories with these details: AVI format، 120×160 pixels resolution and 15 fps. Simulation of algorithm distinguishes the proposed system from similar ones، particularly due to its intelligent monitoring and its real time detection of the elderly’s fall. The average accuracy (AAC)، detection rate (DR) and insignificant false alarm rate (FAR) are 94%، 92. 91% and 5. 52% respectively in acceptable level. The 92% sensitivity and 94. 47% specificity indicate the ability of the system in identifying the incidents similar to the fall. Many advantages such as high speed in data processing، unique accuracy and sensitivity and time parsimony make a system has particular performance and implementation of it due to intelligent monitoring and Real-Time tracking of seniors in Health Care Center and houses.

Studies show that 25% to 47% of elderly will at least once experience falls and this figure is approximately 50% among the elderly living in nursing home. In this paper, based on the Gaussian Mixture Model (GMM) and estimating their parameters by Expectation Maximization (EM) algorithm, a new method has been proposed that firstly, the binary movement of the elderly is segmented from video sequences. Next, the occurrence of falls in older persons is done relying on anatomic body changes and Motion History Images (MHI). Elevation of the system performance was set up on a set of video frames received from the elderly residing in Mother Health Care Center in Sabzevar city and CAVIAR database containing the actual occurrence the of falling. Then, based on the standard deviation and the C-motion coefficient of the walking, suspected incident falls and actual falls are accurately segregated and finally, the sensitivity of 92.68% and the specificity of 96% were obtained which represent a desired capability of the output system. In overall, appropriate simulation of algorithms on the data set due to low error rate in which is less than 6% and meanwhile a careful monitoring of the elderly’s falls will be provided by implementing this system in elderly nursing and residential homes.

Heart and cardiovascular diseases is considered as a major cause of increased mortality worldwide. Listening to the audio signal of the heart is effectively simple and non-invasive technique to detect the irregularities in heart function so that the correct analysis of it requires specialist knowledge and experience. The purpose of this paper is to design and implement an intelligent system for the isolation and classification of cardiac arrhythmias in the audio signal. We received heart sound signals through the heart sound recorder (PCG) in this system. The database were obtained from 41 informed volunteers and non-intervention form during a one month period from Cardiovascular Clinical Research Center Hospital in Sabzevar Vasei which included 15 patients with heart disease (10 males and 5 females with a mean age of 61±5. 7 years) and 26 healthy volunteers (19 males and 7 females with a mean age of 56. 5±8. 7 years) and finally the data were normalized. By testing the samples، 78 normal and 45 abnormal heart sound signals was selected from all subjects. By using adaptive filter، noise and environmental disturbances are extracted from the audio signals in pre-processing step of data. Feature extraction process will do based on cardiac cycles of sound signal in next step by applying the CWT transform on segmented signals and the 32-dimensions matrix of feature vectors will made up using wavelet coefficients component. The final classification of normal and abnormal heart sound signals is accomplished by using multi-layer perceptron neural network and back-propagation (MLP-BP). 70% and 30% of the data were used for training and testing the proposed neural network respectively based on our experiences. The proposed algorithm isolated original signal from noise signal and classified normal and abnormal sound signals at an acceptable level with an average accuracy of 96. 90%±1. 5% and 94%، respectively. This system has the potential for implementation in residential homes for people with different ages due to the reliability of the output of software in correct classification of audio signals and finally a person would be aware of his heart health.

In recent years a new method, called minimal invasive surgery (MIS), has been welcomed. In addition to creating enormous development in the field of surgery, it has corrected defects and aggressive face of this field. MIS usually uses endoscopes and is done through small holes on the skin or through the body cavities, such as airways, digestive and etc. and has less injury and side effects for patients. The main problem of MIS in comparison to open surgeries is the lost of surgeon’s vision in finding the endoscope's position or other surgical instruments inside the body that needs more experience and increase probability of injury to critical tissues. To solve this problem, in recent years surgical navigation systems have been developed, and a kind of these systems was invented and used in Iran in 2011. The main task of a surgical navigation system is to estimate the position of the surgical instrument and how to move it inside the body, and showing this data on the pre-operative images, which were taken from the patients. Experiments show that surgical navigation systems improve endoscopic surgery’s quality and reduce its complications in patients. Therefore these systems will be one of the important requirements of all operating rooms in future. So, due to the rapid advancement of this technology and medical community’s need and especially surgeon’s need to be familiar with surgical navigation system, we have carefully discussed. In this article various parts of these systems, their performance and some of their advantages in an endoscopic surgery has been carefully discussed and reviewed.

Edge detection in image processing is one of the main techniques used in segmentation, separation and detection of the special parts of the image. The presence of noise and structural anomaly due to the weak local contrast of the medical images are of the reasons that prevent the current operators from accurate detection of the edge in these images. In this paper, the metaheuristic colony algorithm has been used for edge detection in medical images. Rapid convergence to obtain the optimal solution along with the parameters resistant to initialization has increased the efficiency of the algorithm. In different parts of the image, especially the part with pathological damage, the edge is assumed as ant’s food. Receiving 222 medical images composed of 02 retina images taken from diabetic patients, 02 MRI images as well as 02 microscopic images taken from various medical databases and applying system to them in contrast to such known operators as Canny and Sobel, an acceptable level of accuracy 09. 02%, sensitivity 09. 61% and specificity 09% was separated in the target area from the rest of image. The 00. 90% Kappa coefficient indicates the high reliability factor of system in terms of performance. The use of the current combination method for processing of the images has increased the accuracy even in images with high brightness, rendering the F-Measure significant. The accurate extraction of pathological parts from medical images allows the specialist to determine the disease progression stage, and suggest an appropriate treatment in accordance with the disease growth.

Developing intelligent systems to prevent car accidents can be very effective in minimizing accident death toll. One of the factors which play an important role in accidents is the human errors. Fatigue driving is one of the cases that can cause errors and reduce accuracy in contorlling the vehicle. The signs of fatigue and sleepiness while driving is revealed based on video processing techniques. In this model، the person’s face is filmed by a camera in the first step by receiving 15fps video sequence. Then، the images are transformed from RGB space into YCbCr and HSV spaces. The face area is separated from other parts. That the eyes are open or closed in a specific time interval is determined by focusing on thresholding and equations concerning the symmetry of human faces a finally using K-means Clustering، the frequency of yawning is identified. In the evaluation phase، the implementation of the algorithm on video sequences recorded under real conditions in urban and suburban areas and compared with methods such as Visual Information and Monitoring Driver Vigilance، better performance was achieved. The proposed system has been implemented on different video sequences with average accuracy of 93. 18% and detection rate (DR) of 92. 71% out of total 5900 image frames. High accuracy in segmentation، low error rate and quick processing of input data distinguishes this system from similar ones. This system can minimize the number of accidents caused by drivers’ fatigue