Sharif Digital Repository

Several methods have been proposed to construct Statistical Shape Model (SSM) to aim image analysis using computer in field Computer Aided Diagnosis (CAD), Computer Assisted Surgery (CAS), and other medical applications by providing a prior knowledge. The major challenge for liver shape model is a high variation in geometry such as size, shape and volume between livers. In this paper, we have presented a new technique for the automatic Multi-Scale Statistical Shape Model (MS-SSM) of three-dimensional (3-D) liver from volumetric segmented images data. The procedure included both building of Spherical Harmonics shape description and the Wavelet transform. Principal Component Analysis (PCA) was... 

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty agematched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing... 

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best... 

Aim: The aim of this study was to search for metabolic biomarkers of Crohn's disease (CD). Background: Crohn's disease (CD) is a type of inflammatory bowel disease that causes a wide variety of symptoms. CD can influence any part of the gastrointestinal tract from mouth to anus. CD is not easily diagnosed because monitoring tools are currently insufficient. Thus, the discovery of proper methods is needed for early diagnosis of CD. Patients and methods: We utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (1HNMR) to find the metabolites in serum. Classification of CD and healthy subject was done using partial least squares discriminant analysis (PLS-DA).... 

Objective: Epileptic seizure detection is a key step for epilepsy assessment. In this work, using the pentylenetetrazole (PTZ) model, seizures were induced in rats, and ECoG signals in interictal, preictal, ictal, and postictal periods were recorded. The recorded ECoG signals were then analyzed to detect epileptic seizures in the epileptic rats. Methods: Two different approaches were considered in this work: thresholding and classification. In the thresholding approach, a feature is calculated in consecutive windows, and the resulted index is tracked over time and compared with a threshold. The moment the index crosses the threshold is considered as the moment of seizure onset. In the... 

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization... 

The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15–30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the... 

Introduction: In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. Methods: A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. Results: After applying... 

Coronavirus disease 2019 (COVID-19) is now of global concern due to its rapid dissemination across the globe. The rapid spread of this viral infection, along with many of its unknown aspects, has posed new challenges to the health care systems. The main challenging effects of COVID-19 are rapid dissemination through close contact and varying clinical severity among different individuals. Furthermore, the medical staff in endemic areas are becoming exhausted and deal with a considerable level of job burnout, which can negatively affect their medical decision making. Also, due to the variable pulmonary manifestations of COVID-19, some physicians may misdiagnose patients. To overcome these... 

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to... 

Atherosclerosis is a major cause of coronary artery disease leading to morbidity and mortality worldwide. Currently, coronary angiography is considered to be the most accurate technique to diagnose coronary artery disease (CAD). However, this technique is an invasive and expensive procedure with risks of serious complications. Since the symptoms of CAD are not noticed until advanced stages of the disease, early and effective diagnosis of CAD is considered a pertinent measure. In this paper, a non-invasive optical signal, the finger photoplethysmogram (PPG) obtained before and after reactive hyperemia is investigated to discriminate between subjects with different CAD conditions. To this end,... 

This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate identification of LB patients. 24 healthy individuals and 28 low back pain patients performed trunk motion tasks in five different directions for validation. Four combinations of these motions were selected based on literature, and the corresponding kinematic data was collected. Upon filtering (4th order, low pass Butterworth filter) and normalizing the data, Principal Component Analysis was used for feature extraction, while Support Vector Machine... 

Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests, therefore, an efficient approach for accurate prediction of the... 

Sulfur mustard (SM) is a vesication chemical warfare agent for which there is currently no antidote. Despite years of research, there is no common consensus about the pathophysiological basis of chronic pulmonary disease caused by this chemical warfare agent. In this study, we combined chemometric techniques with nuclear magnetic resonance (NMR) spectroscopy to explore the metabolic profile of sera from SM-exposed patients. A total of 29 serum samples obtained from 17 SM-injured patients, and 12 healthy controls were analyzed by Random Forest. Increased concentrations of seven amino acids, glycerol, dimethylamine, ketone bodies, lactate, acetate, citrulline and creatine together with the... 

Background: Glucose sensors have been extensively researched in patent studies and manufactured a tool for clinical diabetes diagnosis. Although some kinds of electrochemical enzymatic glucose sensors have been commercially successful, there is still room for improvement, in selectivity and reliability of these sensors. Because of the intrinsic disadvantages of enzymes, such as high fabrication cost and poor stability, non-enzymatic glucose sensors have recently been promoted as next generation diagnostic tool due to their relatively low cost, high stability, prompt response, and accuracy. Objective: In this research, a novel free standing and binder free non-enzymatic electrochemical sensor... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a... 

Prostate cancer (PCa), the second most malignant neoplasm in men, is also the fifth leading cause of cancer-related deaths in men globally. Unfortunately, this malignancy remains largely asymptomatic until late-stage emergence when treatment is limited due to the lack of effective metastatic PCa therapeutics. Due to these limitations, early PCa detection through prostate-specific antigen (PSA) screening has become increasingly important, resulting in a more than 50% decrease in mortality. Conventional assays for PSA detection, such as enzyme-linked immunosorbent assay (ELISA), are labor intensive, relatively expensive, operator-dependent and do not provide adequate sensitivity.... 

The primary aim in pre-surgical evaluations in patients with neurological disorders such as epilepsy is determining the precise location of the cortical region responsible for the malfunctions which is called source localization. Different modalities unravel different views of brain activity. Combining these complementary aspects of the brain yields more accurate source localization. In this paper, a method is proposed for combining localization methods in different modalities based on the theory of evidence, the result of some localization methods in modalities are integrated using weights in accordance to their relative performance and are combined using Dempster's rule of combination and... 

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small... 

Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination...