فهرست مطالب

Journal of Medical Signals and Sensors
Volume:6 Issue: 1, Jan-Mar 2016

  • تاریخ انتشار: 1394/11/20
  • تعداد عناوین: 8
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  • Parinaz Mortaheb, Mehdi Rezaeian Page 1
    Segmentation and three‑dimensional (3D) visualization of teeth in dental computerized tomography (CT) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. On the other hand, dental CT image segmentation is a difficult process because of the specific characteristics of the tooth’s structure. This paper presents a method for automatic segmentation of dental CT images. We present a multi‑step method, which starts with a preprocessing phase to reduce the metal artifact using the least square support vector machine. Integral intensity profile is then applied to detect each tooth’s region candidates. Finally, the mean shift algorithm is used to partition the region of each tooth, and all these segmented slices are then applied for 3D visualization of teeth. Examining the performance of our proposed approach, a set of reliable assessment metrics is utilized. We applied the segmentation method on 14 cone‑beam CT datasets. Functionality analysis of the proposed method demonstrated precise segmentation results on different sample slices. Accuracy analysis of the proposed method indicates that we can increase the sensitivity, specificity, precision, and accuracy of the segmentation results by 83.24%, 98.35%, 72.77%, and 97.62% and decrease the error rate by 2.34%. The experimental results show that the proposed approach performs well on different types of CT images and has better performance than all existing approaches. Moreover, segmentation results can be more accurate by using the proposed algorithm of metal artifact reduction in the preprocessing phase.
    Keywords: Computerized tomography image, mean shift, support vector machine, tooth segmentation
  • Amirehsan Lashkari, Mohammad Firouzmand, Fatemeh Pak Page 12
    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 many pathological studies more than 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy.Infra-red breast thermography is an imaging technique based on recording temperature distribution patterns of breast tissue. Compared with breast mammography technique, thermography is more suitable technique because it is noninvasive, non-contact, passive and free ionizing radiation.In this paper, 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 has been 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 minimum Redundancy and Maximum Relevance (mRMR), Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Floating Forward Selection (SFFS), Sequential Floating Backward Selection (SFBS) and Genetic Algorithm (GA) have been used at step 3. Finally to classify and TH labeling procedures, different classifiers such as AdaBoost, Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naïve Bayes (NB) and probability Neural Network (PNN) are assessed to find the best suitable one. These steps are applied on different thermogram images.The results obtained on native database showed the best and significant performance of the proposed algorithm in comprise to the similar studies. According to experimental results, GA combined with AdaBoost with the mean accuracy of 85.33% and 87.42% on the left and right breast images with 0 degree, GA combined with AdaBoost with mean accuracy of 85.17% on the left breast images with 45 degree and mRMR combined with AdaBoost with mean accuracy of 85.15% on the right breast images with 45 degree, and also GA combined with AdaBoost with a mean accuracy of 84.67% and 86.21%, on the left and right breast images with 90 degree, are the best combinations of feature selection and classifier for evaluation of breast images.
    Keywords: Breast cancer, breast thermography, classification, feature selection, TH, thermogram
  • Masoud Kashefpoor, Hossein Rabbani, Majid Barekatain Page 25
  • Daryoush Shahbazi, Gahrouei, Mehri Damoori, Mohammad Bagher Tavakoli, Masoud Moslehi Page 33
    To improve the accuracy of the activity quantification and the image quality in scintigraphy, scatter correction is a vital procedure. The aim of this study is to compare the accuracy in calculation of absorbed dose to patients following bone scan with 99mTc-marked diphosphonates (99mTc-MDP) by two different methods of background correction in conjugate view method. This study involved 22 patients referring to the Nuclear Medicine Center of Shahid Chamran Hospital, Isfahan, Iran. After the injection of 99mTc-MDP,whole-body images from patients were acquired at 10, 60, 90, and 180 min. Organ activities were calculated using the conjugate view method by Buijs and conventional background correction. Finally, the absorbed dose was calculated using the Medical Internal Radiation Dosimetry (MIRD) technique. The results of this study showed that the absorbed dose per unit of injected activity (rad/mCi) ± standard deviation for pelvis bone, bladder, and kidneys by Buijs method was 0.19 ± 0.05, 0.08 ± 0.01, and 0.03 ± 0.01 and by conventional method was 0.13 ± 0.04, 0.08 ± 0.01, and 0.024 ± 0.01, respectively. This showed that Buijs background correction method had a high accuracy compared to conventional method for the estimated absorbed dose of bone and kidneys whereas, for the bladder, its accuracy was low.
    Keywords: Absorbed dose, background correction, conjugate view method, cumulated activity, MIRD
  • Seyyed Mohammadreza Nouri, Mohammad Mikaeili Page 39
    This study investigates the detection of the drowsiness state for a future application such as in the reduction ofthe road traffic accidents. The Electroencephalography(EEG), Electrooculography (EOG), Driving Quality (DQ), and Karolinska Sleepiness Scale (KSS) data of 7 male during approximately 20 hours of sleep deprivation were recorded. To reduce the eye blink artifact, an automatic mechanism based on the Independent Component Analysis (ICA) method and Higuchi’s fractal dimension has been applied. Afterrecordings, for selecting the best subset of features, a new combined method, called Class Separability Feature Selection- Sequential Feature Selection (CSFS-SFS), has been developed. This method reduces the time of calculations from 6807 to 2096 seconds(by 69.21%)while the classification accuracyremain relatively unchanged. For diagnosis of the drowsiness state and classification of the state, a new approach based on a Self Organized Map (SOM) network is used. First, using the data obtained from two classes of awareness state (AS) and drowsiness state (DS), the network achieved an accuracy of 76.51±3.43%. Using data from three classes of AS, AS/DS (passing from awareness to drowsiness), and DS to the network an accuracy of62.70±3.65% was achieved. It is suggested that the drowsiness state during driving is detectable with an unsupervised network.
    Keywords: Driving drowsiness, eye blink artifact, feature selection, independent component analysis, self‑organized map network
  • Hassan Yazdanian, Amin Mahnam, Mehdi Edrisi, Morteza Abdaresfahani Page 47
    Measurement of the stroke volume and its changes over time can be very helpful for diagnosis of dysfunctions in the blood circulatory system and monitoring their treatments. Impedance cardiography(ICG)is a simple method of measuring the stroke volume based on changes in the instantaneous mean impedanceof the thorax. This method has received much attention in the last two decades because it is non-invasive, easy to be used, and applicable for continuous monitoring of the stroke volumeas well as otherhemodynamic parameters.The aim of this study was to developa low cost portable ICG system with high accuracy for monitoring stroke volume.The proposed wireless system uses a tetrapolar configuration to measure the impedance of the thorax at 50 kHz. The system consists ofcarefully designed precise voltage-controlled current source,biopotential recorder anddemodulator. The measured impedance was analyzed on the computer to determine the stroke volume. After evaluating the system’s electronic performance, its accuracy was assessed by comparing its measurements with the values obtained from Doppler Echocardiography on 5 participants. An impedance cardiograph was implemented that can noninvasively provide a continuous measure of the stroke volume. The SNR of the system was measured above 50dB. The experiments revealed that a strongcorrelation (r=0.89) exists between the measurements by the developed system and Doppler-echocardiograph (p<0.05). ICG as the sixth vital sign can be measured simply and reliably by the developed system, but more detailed validation studies should be conducted to evaluate the system performance. There is a good promise to upgrade the system to a commercial version domestically for clinical use in the future.
    Keywords: Hemodynamics, impedance cardiography, impedance plethysmography, stroke volume
  • Mina Jamshidia, Hossein Rabbani, Zahra Amini, Raheleh Kafieh, Abbas Ommani, Vasudevan Lakshminarayanand Page 57
    Alzheimer’s disease (AD) is one of the most expensive and fatal disease in elderly population. Up to now no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild Cognitive Impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory and speech not too severe to interfere daily activities. MCI diagnosis israther hard and usually assumed as normal consequences of aging. This study, proposes an accurate, mobile and non-expensive diagnostic approach based on EEG signal. EEG signals were recorded using 19 electrodes positioned according to the 10–20 International system at resting eyes closed state from 16 normal and 11 MCI participants. 19 Spectral features are computed for each channel and examined using a correlation-based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and KNN classifier. Final results reach 88.89%, 100% and 83.33% for accuracy, sensitivity and specificity respectively which shows the potential of proposed method to be used as a MCI diagnostic tool especially for screening a large population.
    Keywords: Early Alzheimer's disease, electroencephalogram spectral features, k‑nearest neighbor, mild cognitive impairment, neurofuzzy
  • Omid Sarrafzadeh, Neda Esmaeili Page 64
    The iBridge Berlin conference was a 3-day event which brought together Iranian entrepreneurs and business owners and their counterparts in Europe and the US to explore opportunities in Iran’s high-tech sector. It was a pure educational event, privately funded, and organized with no government involvement.