Showing papers in "Journal of medical signals and sensors in 2016"

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

Abstract: Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features.

Abstract: Alzheimer's disease (AD) is one of the most expensive and fatal diseases in the 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 is rather hard and usually assumed as normal consequences of aging This study proposes an accurate, mobile, and nonexpensive diagnostic approach based on electroencephalogram (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 Nineteen 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 k-nearest neighbor classifier Final results reach 8889%, 100%, and 8333% for accuracy, sensitivity, and specificity, respectively, which shows the potential of proposed method to be used as an MCI diagnostic tool, especially for screening a large population

Full Intelligent Cancer Classification of Thermal Breast Images to Assist Physician in Clinical Diagnostic Applications.

Abstract: 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 75% - 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), Naive Bayes (NB) and probability Neural Network (PNN) are assessed to find the best suitable one. These steps are applied on different thermogram images degrees. 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.

Three-dimensional Segmentation of Retinal Cysts from Spectral-domain Optical Coherence Tomography Images by the Use of Three-dimensional Curvelet Based K-SVD.

Abstract: This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinalcysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherencetomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, andshow the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximatethe corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce anew scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in eachscale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted withgraph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume andwith-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Multiple MTS Assay as the Alternative Method to Determine Survival Fraction of the Irradiated HT-29 Colon Cancer Cells.

Abstract: A multiple colorimetric assay has been introduced to evaluate the proliferation and determination of survival fraction (SF) of irradiated cells. The estimation of SF based on the cell-growth curve information is the major advantage of this assay. In this study, the utility of multiple-MTS assay for the SF estimation of irradiated HT-29 colon cancer cells, which were plated before irradiation, was evaluated. The SF of HT-29 colon cancer cells under irradiation with 9 MV photon was estimated using multiple-MTS assay and colony assay. Finally, the correlation between two assays was evaluated. Results showed that there are no significant differences between the SF obtained by two assays at different radiation doses (P > 0.05), and the survival curves have quite similar trends. In conclusion, multiple MTS-assay can be a reliable method to determine the SF of irradiated colon cancer cells that plated before irradiation.

Design and Implementation of a Portable Impedance Cardiography System for Noninvasive Stroke Volume Monitoring.

Abstract: Measurement of the stroke volume (SV) 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 SV based on changes in the instantaneous mean impedance of the thorax. This method has received much attention in the last two decades because it is noninvasive, easy to be used, and applicable for continuous monitoring of SV as well as other hemodynamic parameters. The aim of this study was to develop a low-cost portable ICG system with high accuracy for monitoring SV. The proposed wireless system uses a tetrapolar configuration to measure the impedance of the thorax at 50 kHz. The system consists of carefully designed precise voltage-controlled current source, biopotential recorder, and demodulator. The measured impedance was analyzed on a computer to determine SV. After evaluating the system's electronic performance, its accuracy was assessed by comparing its measurements with the values obtained from Doppler echocardiography (DE) on 5 participants. The implemented ICG system can noninvasively provide a continuous measure of SV. The signal to noise ratio of the system was measured above 50 dB. The experiments revealed that a strong correlation (r = 0.89) exists between the measurements by the developed system and DE (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.

Aptamer-conjugated Magnetic Nanoparticles as Targeted Magnetic Resonance Imaging Contrast Agent for Breast Cancer.

Abstract: Early detection of breast cancer is the most effective way to improve the survival rate in women. Magnetic resonance imaging (MRI) offers high spatial resolution and good anatomic details, and its lower sensitivity can be improved by using targeted molecular imaging. In this study, AS1411 aptamer was conjugated to Fe3O4@Au nanoparticles for specific targeting of mouse mammary carcinoma (4T1) cells that overexpress nucleolin. In vitro cytotoxicity of aptamer-conjugated nanoparticles was assessed on 4T1 and HFFF-PI6 (control) cells. The ability of the synthesized nanoprobe to target specifically the nucleolin overexpressed cells was assessed with the MRI technique. Results show that the synthesized nanoprobe produced strongly darkened T2-weighted magnetic resonance (MR) images with 4T1 cells, whereas the MR images of HFFF-PI6 cells incubated with the nanoprobe are brighter, showing small changes compared to water. The results demonstrate that in a Fe concentration of 45 μg/mL, the nanoprobe reduced by 90% MR image intensity in 4T1 cells compared with the 27% reduction in HFFF-PI6 cells. Analysis of MR signal intensity showed statistically significant signal intensity difference between 4T1 and HFFF-PI6 cells treated with the nanoprobe. MRI experiments demonstrate the high potential of the synthesized nanoprobe as a specific MRI contrast agent for detection of nucleolin-expressing breast cancer cells.

Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals

Abstract: This study investigates the detection of the drowsiness state (DS) for future application such as in the reduction of the road traffic accidents. The electroencephalography, electrooculography, driving quality, and Karolinska sleepiness scale data of 7 males during approximately 20 h of sleep deprivation were recorded. To reduce the eye blink artifact, an automatic mechanism based on the independent component analysis method and Higuchi's fractal dimension has been applied. After recordings, for selecting the best subset of features, a new combined method, called class separability feature selection-sequential feature selection, has been developed. This method reduces the time of calculations from 6807 to 2096 s (by 69.21%) while the classification accuracy remains relatively unchanged. For diagnosis of the DS and classification of the state, a new approach based on a self-organized map network is used. First, using the data obtained from two classes of awareness state (AS) and 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 of 62.70 ± 3.65% was achieved. It is suggested that the DS during driving is detectable with an unsupervised network.

Cepstral Analysis of EEG During Visual Perception and Mental Imagery Reveals the Influence of Artistic Expertise.

Abstract: In this article, multichannel EEG signals of artists and nonartists were analyzed during the performances of visual perception and mental imagery of paintings using cepstrum coefficients. Each of the calculated cepstrum coefficients and their parameters such as energy, average, standard deviation and entropy were separately used for distinguishing the two groups. It was also found that a distinguishing coefficient might exist among the cepstrum coefficients, which could separate the two groups despite electrode placement. It was also observed that the two groups were distinguishable during the three states using the cepstrum coefficient parameters. However, separating the two groups was dependent on channel selection in this regard. The cepstrum coefficient parameters were found significantly lower for artists as compared to nonartists during the visual perception and the mental imagery, indicating a decreased average energy of EEG for artists. In addition, a similar significant decreasing trend in the cepstrum coefficient parameters was observed from occipital to frontal brain regions during the performances of the two cognitive tasks for the two groups, suggesting that visual perception and its mental imagery overlap in neuronal resources. The two groups were also classified using a neural gas classifier and a support vector machine classifier. The obtained average classification accuracies during the visual perception, the mental imagery, and at rest in the case of using the best selected distinguishable cepstrum coefficients were 76.87%, 77.5%, and 97.5%, respectively; however, a decrease in average recognition accuracy was found for classifying the two groups using the cepstrum coefficient parameters.

Radio Frequency Ultrasound Time Series Signal Analysis to Evaluate High-intensity Focused Ultrasound Lesion Formation Status in Tissue.

Abstract: High‑intensity focused ultrasound (HIFU) is a novel treatment modality used by scientists and clinicians in the recent decades. This modality has had a great and significant success as a noninvasive surgery technique applicable in tissue ablation therapy and cancer treatment. In this study, radio frequency (RF) ultrasound signals were acquired and registered in three stages of before, during, and after HIFU exposures. Different features of RF time series signals including the sum of amplitude spectrum in the four quarters of the frequency range, the slope, and intercept of the best‑fit line to the entire power spectrum and the Shannon entropy were utilized to distinguish between the HIFU‑induced thermal lesion and the normal tissue. We also examined the RF data, frame by frame to identify exposure effects on the formation and characteristics of an HIFU thermal lesion at different time steps throughout the treatment. The results obtained showed that the spectrum frequency quarters and the slope and intercept of the best fit line to the entire power spectrum both increased two times during the HIFU exposures. The Shannon entropy, however, decreased after the exposures. In conclusion, different characteristics of RF time series signal possess promising features that can be used to characterize ablated and nonablated tissues and to distinguish them from each other in a quasi‑quantitative fashion.

Breathomics for Gastric Cancer Classification Using Back-propagation Neural Network.

Abstract: Breathomics is the metabolomics study of exhaled air. It is a powerful emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the quantity of these compounds varies with health status, breathomics assures to deliver noninvasive diagnostic tools. Thus, the main aim of breathomics is to discover patterns of VOCs related to abnormal metabolic processes occurring in the human body. Classification systems, however, are not designed for cost-sensitive classification domains. Therefore, they do not work on the gastric carcinoma (GC) domain where the benefit of correct classification of early stages is more than that of later stages, and also the cost of wrong classification is different for all pairs of predicted and actual classes. The aim of this work is to demonstrate the basic principles for the breathomics to classify the GC, for that the determination of VOCs such as acetone, carbon disulfide, 2-propanol, ethyl alcohol, and ethyl acetate in exhaled air and stomach tissue emission for the detection of GC has been analyzed. The breath of 49 GC and 30 gastric ulcer patients were collected for the study to distinguish the normal, suspected, and positive cases using back-propagation neural network (BPN) and produced the accuracy of 93%, sensitivity of 94.38%, and specificity of 89.93%. This study carries out the comparative study of the result obtained by the single- and multi-layer cascade-forward and feed-forward BPN with different activation functions. From this study, the multilayer cascade-forward outperforms the classification of GC from normal and benign cases.

Synthesis and In vitro Evaluation of Electrodeposited Barium Titanate Coating on Ti6Al4V.

Abstract: Osseointegration has been the concern of implantology for many years. Researchers have used various ceramic coatings for this purpose; however, piezoelectric ceramics (e.g., barium titanate [BTO]) are a novel field of interest. In this regard, BTO (BaTiO3) coating was fabricated by electrophoretic deposition on Ti6Al4V medical alloy, using sol-gel-synthesized nanometer BTO powder. Structure and morphologies were studied using X-ray diffraction and scanning electron microscopy (SEM), respectively. Bioactivity response of coated samples was evaluated by SEM and inductively coupled plasma (ICP) analysis after immersion in simulated body fluid (SBF). Cell compatibility was also studied via MTT assay and SEM imaging. Results showed homogenous coating with cubic structure and crystallite size of about 41 nm. SEM images indicated apatite formation on the coating after 7 days of SBF immersion, and ICP analysis approved ions concentration decrement in SBF. Cells showed flattened morphology in intimate contact with coating after 7 days of culture. Altogether, coated samples demonstrated appropriate bioactivity and biocompatibility.

Quantitative Assessment of Retinopathy Using Multi-parameter Image Analysis.

Abstract: A multi-parameter quantification method was implemented to quantify retinal vascular injuries in microscopic images of clinically relevant eye diseases. This method was applied to wholemount retinal trypsin digest images of diabetic Akita/+, and bcl-2 knocked out mice models. Five unique features of retinal vasculature were extracted to monitor early structural changes and retinopathy, as well as quantifying the disease progression. Our approach was validated through simulations of retinal images. Results showed fewer number of cells (P = 5.1205e-05), greater population ratios of endothelial cells to pericytes (PCs) (P = 5.1772e-04; an indicator of PC loss), higher fractal dimension (P = 8.2202e-05), smaller vessel coverage (P = 1.4214e-05), and greater number of acellular capillaries (P = 7.0414e-04) for diabetic retina as compared to normal retina. Quantification using the present method would be helpful in evaluating physiological and pathological retinopathy in a high-throughput and reproducible manner.

iBridge Berlin 2015, City Cube convention center, Berlin, Germany, 4-6 June, 2015

Abstract: 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.

Providing an Efficient Algorithm for Finding R Peaks in ECG Signals and Detecting Ventricular Abnormalities With Morphological Features.

Abstract: Ventricular arrhythmias are one of the most important causes of annual deaths in the world, which may lead to sudden cardiac deaths. Accurate and early diagnosis of ventricular arrhythmias in heart diseases is essential for preventing mortality in cardiac patients. Ventricular activity on the electrocardiogram (ECG) signal is in the interval from the beginning of QRS complex to T wave end. Variations in the ECG signal and its features may indicate heart condition of patients. The first step to extract features of ECG in time domain is finding R peaks. In this paper, a combination of two algorithms of Pan–Tompkins and state logic machine has been used to find R peaks in heart signals for normal sinus signals and ventricular abnormalities. Then, a healthy or sick beat may be realized by comparing the difference between R peaks obtained from two algorithms in each beat. The morphological features of the ECG signal in the range of QRS complex are evaluated. Ventricular tachycardia (VT), ventricular flutter (VFL), ventricular fibrillation (VFI), ventricular escape beat (VEB), and premature ventricular contractions (PVCs) are abnormalities studied in this paper. In the classification step, the support vector machine (SVM) classifier with Gaussian kernel (one in front of everyone) is used. Accuracy percentages of ventricular abnormalities mentioned above and normal sinus rhythm are respectively obtained as 95.8%, 92.8%, 94.5, 98.9%, 91.5%, and 100%. The database of this paper has been taken from normal sinus rhythm and MIT-SCD banks available on Physionet.org.

Metal Artifact Reduction and Segmentation of Dental Computerized Tomography Images Using Least Square Support Vector Machine and Mean Shift Algorithm.

Abstract: 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.

Performance Improvement of the Goertzel Algorithm in Estimating of Protein Coding Regions Using Modified Anti-notch Filter and Linear Predictive Coding Model.

Abstract: The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm.

Evaluation of a low-cost and low-noise active dry electrode for long-term biopotential recording

Abstract: Wet Ag/AgCl electrodes, although very popular in clinical diagnosis, are not appropriate for expanding applications of wearable biopotential recording systems which are used repetitively and for a long time. Here, the development of a low-cost and low-noise active dry electrode is presented. The performance of the new electrodes was assessed for recording electrocardiogram (ECG) and electroencephalogram (EEG) in comparison with that of typical gel-based electrodes in a series of long-term recording experiments. The ECG signal recorded by these electrodes was well comparable with usual Ag/AgCl electrodes with a correlation up to 99.5% and mean power line noise below 6.0 μVRMS. The active electrodes were also used to measure alpha wave and steady state visual evoked potential by recording EEG. The recorded signals were comparable in quality with signals recorded by standard gel electrodes, suggesting that the designed electrodes can be employed in EEG-based rehabilitation systems and brain-computer interface applications. The mean power line noise in EEG signals recorded by the active electrodes (1.3 μVRMS) was statistically lower than when conventional gold cup electrodes were used (2.0 μVRMS) with a significant level of 0.05, and the new electrodes appeared to be more resistant to the electromagnetic interferences. These results suggest that the developed low-cost electrodes can be used to develop wearable monitoring systems for long-term biopotential recording.

Extraction of the Best Frames in Coronary Angiograms for Diagnosis and Analysis.

Abstract: X-ray coronary angiography has been a gold standard in the clinical diagnosis and interventional treatment of coronary arterialdiseases for decades. In angiography, a sequence of images is obtained, a few of which are suitable for physician inspection. Thispaper proposes an automatic algorithm for the extraction of one or more frames from an angiogram sequence, which is most suitablefor diagnosis and analysis by experts or processors. The algorithm consists of two stages: In the first stage, the background andillumination in the angiogram sequence are omitted. By analyzing the histogram of the sequence, a feature is attributed to eachframe. These features, determining the visibility of the vessel tree, are clustered by a fuzzy c-means method. In the second stage, thecardiac phase for each frame is specified. Using the results of both stages, the best frames in an angiogram sequence are obtained.To evaluate the proposed method, it has been tested on angiogram sequences from several patients. The results demonstrate theaccuracy of the method. The performance and speed of our method indicate its usefulness in clinical applications.

A Novel Thresholding Based Algorithm for Detection of Vertical Root Fracture in Nonendodontically Treated Premolar Teeth.

Abstract: In this paper, an efficient algorithm is proposed for detection of vertical root fractures (VRFs) in periapical (PA), and cone-beam computed tomography (CBCT) radiographs of nonendodontically treated premolar teeth. PA and CBCT images are divided into some sub-categories based on the fracture space between the two fragments as small, medium, and large for PAs and large for CBCTs. These graphics are first denoised using the combination of block matching 3-D filtering, and principle component analysis model. Then, we proposed an adaptive thresholding algorithm based on the modified Wellner model to segment the fracture and canal. Finally, VRFs are identified with a high accuracy through applying continuous wavelet transform on the segmented radiographs and choosing the most optimal value for sub-images based on the lowest interclass variance. Performance of the proposed algorithm is evaluated utilizing the different tested criteria. Results illustrate that the range of specificity deviations for PA and CBCT radiographs are 99.69 ± 0.22 and 99.02 ± 0.77, respectively. Furthermore, the sensitivity changes from 61.90 to 77.39 in the case of PA and from 79.54 to 100 in the case of CBCT. Based on our statistical evaluation, the CBCT imaging has the better performance in comparison with PA ones, so this technique could be a useful tool for clinical applications in determining the VRFs.

Barrett's Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy c-mean and Level Set.

Abstract: Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastroesophagus reflux. If left untreated, the disease will cause distal esophagus and gastric cardiaadenocarcinoma. The malignancy risk is very high in short segment Barrett’s mucosa. Therefore,lesion area segmentation can improve specialist decision for treatment. In this paper, we proposeda combined fuzzy method with active models for Barrett’s mucosa segmentation. In this study,we applied three methods for special area segmentation and determination. For whole disease areasegmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithmswere used for gastroesophageal junction determination, and we discriminated Barrett’s mucosa from breakby applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical imagedue to weak boundaries. In contrast, the full automatic hybrid method with correlation approachthat has used in this paper segmented the metaplasia area in the endoscopy image with desirableaccuracy. The presented approach omits the manually desired cluster selection step that needed theoperator manipulation. Obtained results convinced us that this approach is suitable for esophagusmetaplasia segmentation.

Automatic Detection of the Optic Disc of the Retina: A Fast Method.

Abstract: Localizing the optic disc (OD) in retinal fundus images is of critical importance and many techniques have been developed for OD detection. In this paper, we present the results obtained from two fast methods, correlation and least square, to approximate the location of optic cup. These methods are simple and are not complex, while most of the OD detection algorithms are. The methods were tested on two groups of data (a total of 100 color fundus images) and were 98% successful in the detection of the optic cup. An algorithm using the vessel mask of fundus images is proposed to be run after correlation to ensure that the localization of OD in all images is successful. It was tested on 40 of the test images and had a 100% rate of success.

Assessment of the Focal Hepatic Lesions Using Diffusion Tensor Magnetic Resonance Imaging.

Abstract: The goal is assessing the diffusion magnetic resonance imaging (dMRI) method efficiency in characterizing focal hepatic lesions (FHLs). About 28-FHL patients were studied in Radiology and Clinical Imaging Department of our University Hospital using 1.5 Tesla MRI system between January 2010 and June 2011. Patients underwent hepatic MRI consisting of dynamic T1- and T2-weighted imaging. The dMRI was performed with b-values of 200 s/mm(2) and 600 s/mm(2). About 42 lesions measuring more than 1 cm were studied including the variation of the signal according to the b-value and the apparent diffusion coefficient (ADC). The diagnostic imaging reference was based on standard MRI techniques data for typical lesions and on histology after surgical biopsy for atypical lesions. About 38 lesions were assessed including 13 benign lesions consisting of 1 focal nodular hyperplasia, 8 angiomas, and 4 cysts. About 25 malignant lesions included 11 hepatocellular carcinoma, 9 hepatic metastases, 1 cholangiocarcinoma, and 4 lymphomas. dMRI of soft lesions demonstrated higher ADC of 2.26 ± 0.75 mm(2)/s, whereas solid lesions showed lower ADC 1.19 ± 0.33 mm(2)/s with significant difference (P = 0.05). Discrete values collections were noticed. These results were correlated to standard MRI and histological findings. Sensitivity of 93% and specificity of 84% were found in diagnoses of malignant tumors with an ADC threshold of 1.6 × 10(-3) mm(2)/s. dMRI is important characterization method of FHL. However, it should not be used as single criteria of hepatic lesions malignity. MRI, clinical, and biological data must be correlated. Significant difference was found between benign and solid malignant lesions without threshold ADC values. Hence, it is difficult to confirm ADC threshold differentiating the lesion classification.

Effects of Nano-biphasic Calcium Phosphate Composite on Bioactivity and Osteoblast Cell Behavior in Tissue Engineering Applications.

Abstract: In this paper, preparation, bioactivity, and osteoblast cell behavior of cortical bone derived nanobiphasic calcium phosphate (nano-BCP) are presented. The calcined bovine bone samples with the addition of di-ammonium hydrogen phosphate were heated at 700°C for 100 min, and thus nano-BCP with the composition of 63/37 hydroxyapatite (HA)/β-tricalcium phosphate (β-TCP) was produced. Scanning electron microscopy (SEM) images, energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) analysis of immersed samples in simulated body fluid (SBF) solution showed that a uniform layer was formed on the surface after 7 days with the chemical composition of HA. The results indicated that the nano-BCP sample developed excellent bioactivity after 28 days. The nano-BCP samples showed better cell proliferation compared to pure HA samples. After 7 days in cell culture, the prepared nano-BCP (HA/β-TCP) exhibited the maximum proliferation of the MG-63 osteoblast cells.

Human Verification Using a Combination of Static and Dynamic Characteristics in Foot Pressure Images.

Abstract: Since gait is the mixture of many complex movements, each individual can define with a unique foot pressure image that can be used as a reliable biometric scale for human verification. Foot pressure color images of Center for Biometrics and Security Research (CBSR) dataset from 45 men and 5 women were used in this study. Owing to the properties of this dataset, an index of foot pressure in addition to external feature and contourlet coefficient of images was extracted. A multilayer perceptron (MLP) was utilized for verification of subjects (it is a common practice to explain more about the training and test dataset). To validate the algorithm performance, results were obtained using a 5-fold cross validation approach. The results indicated accuracy of 99.14±0.65 and equal error rate (EER) of 0.02. These results demonstrated the reliability of proposed neural network in human verification application. Hence, it can be utilized in other verification systems.

Comparison of Accuracy in Calculation of Absorbed Dose to Patients Following Bone Scan with (99m)Tc-Marked Diphosphonates by Two Different Background Correction Methods.

Abstract: 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 (99m)Tc-marked diphosphonates ((99m)Tc-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 (99m)Tc-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.

Application of Hyperelastic-based Active Mesh Model in Cardiac Motion Recovery.

Abstract: Considering the nonlinear hyperelastic or viscoelastic nature of soft tissues has an important effect on modeling results. In medicalapplications, accounting nonlinearity begets an ill posed problem, due to absence of external force. Myocardium can be consideredas a hyperelastic material, and variational approaches are proposed to estimate stiffness matrix, which take into account the linearand nonlinear properties of myocardium. By displacement estimation of some points in the four-dimensional cardiac magneticresonance imaging series, using a similarity criterion, the elementary deformations are estimated, then using the Moore–Penroseinverse matrix approach, all point deformations are obtained. Using this process, the cardiac wall motion is quantized to mechanicallydetermine local parameters to investigate the cardiac wall functionality. This process was implemented and tested over 10 healthyand 20 patients with myocardial infarction. In all patients, the process was able to precisely determine the affected region. Theproposed approach was also compared with linear one and the results demonstrated its superiority respect to the linear model.

Interdisciplinary Researches in Iran V: Toward Interdisciplinary Technologies.

Abstract: There are different approaches to business model categorization. [5] In a point of view, the E-business model patterns can be categorized to content provider, direct to customer (D2C), full-service provider, intermediary, shared infrastructure, value net integrator, virtual community, and whole of enterprise.

Interdisciplinary Researches in Iran VI: Student Clubs.

Abstract: The current curriculum for both medical and engineering students does not include any serious program for research and technology development, by which capable students can become a man of research and a man with high-tech skills as soon as possible. However, there are opportunities for several good experiences in Iranian universities such as “Research Student Clubs,” [2,3] which show that there exists a huge potential for the student to learn and do research.

Interdisciplinary Researches in Iran IV: The Road Map of Ocular Image Analysis Research Group

Abstract: Nowadays, the world is faced with diverse challenging issues which lead to the development of communities to discuss about the matter and provide solutions for them. Academic research centers are then established to puzzle out the raised questions with reliance on expert opinions and studies in response to the current prevailing needs. Islamic Republic of Iran blessed with knowledgeable academic resources, is currently growing fast in research[1]. A great number of centers are already dedicated to research, commonly with the focus on a specific area. However, the real maturity in research centers demands accurate and precise definition of the goals and solidarity to approach the target. Such a mature center can be expected to deliver valuable products in a predetermined time. It is expected that designing a roadmap would be helpful in achieving such a maturity.