Sharif Digital Repository

In photoacoustic tomography (PAT) systems, the tangential resolution decreases due to the finite size of the transducer as the off-center distance increases. To address this problem, we propose a multi-angle detection approach in which the transducer used for data acquisition rotates around its center (with specific angles) as well as around the scanning center. The angles are calculated based on the central frequency and diameter of the transducer and the radius of the region-of-interest (ROI). Simulations with point-like absorbers (for point-spread-function evaluation) and a vasculature phantom (for quality assessment), and experiments with ten 0.5 mm-diameter pencil leads and a leaf... 

Background: Longitudinal data on the association of quantitative thigh muscle MRI markers with knee osteoarthritis (KOA) outcomes are scarce. These associations are of clinical importance, with potential use for thigh muscle–directed disease-modifying interventions. Purpose: To measure KOA-associated longitudinal changes in MRI-derived muscle cross-sectional area (CSA) and adipose tissue and their association with downstream symptom worsening and knee replacement (KR). Materials and Methods: In a secondary analysis of the Osteoarthritis Initiative multicenter prospective cohort (February 2004 through October 2015), knees of participants with available good-quality thigh MRI scans at baseline... 

One of the most important goals of any organization for a product that goes into the design or manufacturing process is to improve its reliability. Reliability growth is defined as improving a product's criteria (input parameters) during operation using changes in design or production. This paper presents a hardware-centric approach for the reliability growth of systems following normal distribution based on the Non-Homogeneous Poisson Process (NHPP). To reach this goal, the reliability growth modeling equations for the NHPP with the assumption of the normal distribution for failure data are extracted and obtained. Then, the maximum likelihood estimation technique based on the... 

Curcumin incorporated polyurethanes (CPU) are gaining much attention as a biomaterial. However, challenges are still remained due to hydrophobicity and low mechanical strength of CPU. Herein, we synthesized the CPU/ZnO nanocomposites with good mechanical and improved hydrophilic properties via in-situ polymerization. A series of curcumin incorporated polyurethane with different concentrations of ZnO nanoparticles (ZnCPU) are synthesized by using the curcumin, polyethylene glycol (PEG) as the soft segment, hexamethylene diisocyanate (HDI) as the hard segment, and 1,4-butanediol (BDO) as the chain extender. The addition of ZnO nanoparticles (NPs) facilitated the soft domain of PU which is... 

Background: Remote cardiac monitoring and screening have already become an integral telemedicine component. The wide usage of several different wireless electrocardiography (ECG) devices warrants a validation study on their accuracy and reliability. Methods: Totally, 300 inpatients with the Nabz Hooshmand-1 handheld ECG device and the GE MAC 1200 ECG system (as the reference) were studied to check the accuracy of the devices in 1 and 6-limb lead performance. Simultaneous 10-second resting ECGs were assessed for the most common ECG parameters in lead I. Afterward, 6-lead ECGs (limb leads), were performed immediately and studied for their morphologies. Results: Of the 300 patients, 297 had... 

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of 12 state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and... 

Purpose: This study exploits a novel musculoskeletal finite element (MS-FE) spine model to evaluate the post-fusion (L4–L5) alterations in adjacent segment kinetics. Methods: Unlike the existing MS models with idealized representation of spinal joints, this model predicts stress/strain distributions in all passive tissues while organically coupled to a MS model. This generic (in terms of musculature and material properties) model uses population-based in vivo vertebral sagittal rotations, gravity loads, and an optimization algorithm to calculate muscle forces. Simulations represent individuals with an intact L4–L5, a preoperative severely degenerated L4–L5 (by reducing the disc height by ~... 

Passive finite element (FE) models of the spine are commonly used to simulate intact and various pre- and postoperative pathological conditions. Being devoid of muscles, these traditional models are driven by simplistic loading scenarios, e.g., a constant moment and compressive follower load (FL) that do not properly mimic the complex in vivo loading condition under muscle exertions. We aim to develop novel passive FE models that are driven by more realistic yet simple loading scenarios, i.e., in vivo vertebral rotations and pathological-condition dependent FLs (estimated based on detailed musculoskeletal finite element (MS-FE) models). In these novel force–displacement control FE models,... 

Karyotype is a genetic test that is used for detection of chromosomal defects. In a karyotype test, an image is captured from chromosomes during the cell division. The captured images are then analyzed by cytogeneticists in order to detect possible chromosomal defects. In this paper, we have proposed an automated pipeline for analysis of karyotype images. There are three main steps for karyotype image analysis: image enhancement, image segmentation and chromosome classification. In this paper, we have proposed a novel chromosome segmentation algorithm to decompose overlapped chromosomes. We have also proposed a CNN-based classifier which outperforms all the existing classifiers. Our... 

Crack detection is a vital component of structural health monitoring. Several computer vision-based studies have been proposed to conduct crack detection on concrete surfaces, but most cases have difficulties in detecting fine cracks. This study proposes a deep learning-based model for automatic crack detection on the concrete surface. Our proposed model is an encoder–decoder model which uses EfficientNet-B7 as the encoder and U-Net’s modified expansion path as the decoder. To overcome the challenges in the detection of fine cracks, we trained our model with a new training strategy on images extracted from an open-access dataset and achieved a 96.98% F1 score for unseen test data. Moreover,... 

According to the WHO, two individuals die every hour from skin cancer and about 9500 people get skin cancer every day just in the United States. Various computer vision algorithms have been introduced for skin lesion detection, classification, and segmentation. This paper proposes a new segmentation-based algorithm in order to select target components using the persistence diagram of the input images. The results, in comparison with the existing seven different both clustering-and histogram-based segmentation methods using three metrics, show improved performance. Medical image segmentation is an essential task in computer-aided diagnosis. The main improvement of our method is to detect one... 

The visibility graph of a polygon corresponds to its internal diagonals and boundary edges. For each vertex on the boundary of the polygon, we have a vertex in this graph and if two vertices of the polygon see each other there is an edge between their corresponding vertices in the graph. Two vertices of a polygon see each other if and only if their connecting line segment completely lies inside the polygon. Recognizing visibility graphs is the problem of deciding whether there is a simple polygon whose visibility graph is isomorphic to a given graph. Another important problem is to reconstruct such a polygon if there is any. These problems are well known and well-studied, but yet open... 

The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performance of the multivariate ordinary least squares (OLS), a simultaneous mean-variance regression (MVR) done by a penalized weighted least-square loss function, and a mean-covariance/variance regression based on expectation maximization method (EM) are assessed. The efficiency of the introduced methods is compared with FEMA-P58 methodology. Performance assessment of EM and MVR methods shows that the overall increase in efficiency is about 25–45% for maximum inter-story drift ratios,... 

Subway metro systems are one of the most efficient modes of mass transportation, especially in heavily populated areas. A series of 3D Finite Element Analysis (FEA) has been carried out to investigate the interaction between mechanized twin tunnels excavated beneath the highly congested streets of Shiraz (Iran). Firstly, the reference numerical model has been verified using the measured data of surface settlement control pins. The effects of driving of the new closed shield tunnel on the evolution of stress fields in the existing segmental lining as well as the ground surface settlements in the reference numerical model have been then quantified. Lastly, the effects of influencing factors... 

A new single-pixel Fourier photoacoustic microscopy (PAM), to the best of our knowledge, is proposed to improve the resolution and region of interest (ROI) of an acquired image. In the previous structure of single-pixel Fourier PAM, called spatially invariant resolution PAM (SIR-PAM), the lateral resolution and ROI are limited by the digital micromirror device (DMD) pixel size and the number of pixels. This limitation is overcome here through illuminating fixed angle interfering plane waves, changing the fringe frequency via varying the frequency of the laser source. Given that the fringe sinusoidal patterns here can be produced by two mirrors, the DMD usage can be omitted. In this way, the... 

Body posture measurement approaches, required in biomechanical models to assess risk of musculoskeletal injuries, are usually costly and/or impractical for use in real workplaces. Therefore, we recently developed three artificial neural networks (ANNs), based on measured posture data on several individuals, to predict whole body 3D posture (coordinates of 15 markers located on body's main joints), segmental orientations (Euler angles of 14 body segments), and lumbosacral (L5-S1) moments during static manual material handling (MMH) activities (ANNPosture, ANNAngle, and ANNMoment, respectively). These ANNs require worker's body height, body weight (only for ANNMoment), hand-load 3D position,... 

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... 

Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information... 

Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose... 

Measurements of spinal segment ranges of motion (RoMs), movement coordination, and three-dimensional kinematics during occupational activities have implications in occupational/clinical biomechanics. Due to the large amount of adipose tissues, obese individuals may have different RoMs, lumbopelvic coordination, and kinematics than normal-weight ones. We aimed to measure/compare trunk, lumbar, and pelvis primary RoMs in all anatomical planes/directions, lumbopelvic ratios (lumbar to pelvis rotations at different trunk angles) in all anatomical planes/directions and three-dimensional spine kinematics during twelve symmetric/asymmetric statics load-handling activities in healthy normal-weight...