Journal of Medical Signals and Sensors
Volume:4 Issue: 1, Jan-Mar 2014

In this paper, a novel matched filter based on a new kernel function with Cauchy distribution is introduced to improve the accuracy ofthe automatic retinal vessel detection compared with other available matched filter‑based methods, most notably, the methods builton Gaussian distribution function. Several experiments are conducted to pick the best values of the parameters for the new designedfilter, including both Cauchy function parameters as well as the matched filter parameters such as the threshold value. Moreover,the thresholding phase is enhanced with a two‑step procedure. Experimental results employed on DRIVE retinal images databaseconfirms that the proposed method has higher accuracy compared with other available matched filter‑based methods.

Radiotherapy dose calculations can be evaluated by Monte Carlo (MC) simulations with acceptable accuracy for dose prediction in complicated treatment plans. In this work, Standard, Livermore and Penelope electromagnetic (EM) physics packages of GEANT4 application for tomographic emission (GATE) 6.1 were compared versus Monte Carlo N-Particle eXtended (MCNPX) 2.6 in simulation of 6 MV photon Linac. To do this, similar geometry was used for the two codes. The reference values of percentage depth dose (PDD) and beam profiles were obtained using a 6 MV Elekta Compact linear accelerator, Scanditronix water phantom and diode detectors. No significant deviations were found in PDD, dose profile, energy spectrum, radial mean energy and photon radial distribution, which were calculated by Standard and Livermore EM models and MCNPX, respectively. Nevertheless, the Penelope model showed an extreme difference. Statistical uncertainty in all the simulations was <1%, namely 0.51%, 0.27%, 0.27% and 0.29% for PDDs of 10 cm2 × 10 cm2 filed size, for MCNPX, Standard, Livermore and Penelope models, respectively. Differences between spectra in various regions, in radial mean energy and in photon radial distribution were due to different cross section and stopping power data and not the same simulation of physics processes of MCNPX and three EM models. For example, in the Standard model, the photoelectron direction was sampled from the Gavrila-Sauter distribution, but the photoelectron moved in the same direction of the incident photons in the photoelectric process of Livermore and Penelope models. Using the same primary electron beam, the Standard and Livermore EM models of GATE and MCNPX showed similar output, but re-tuning of primary electron beam is needed for the Penelope model.

Ultrasoundelastographyisanon-invasivemethodwhichimagestheelasticityofsoft-tissues. Tomakeanimage, preandafterasmallcompression, ultrasoundradio frequency(RF signalsareacquiredandthetimedelaysbetweenthemareestimated. Thefirstdifferentiationofdisplacementestimationsiscalledelastogram. Inthisstudy, wearegoingtomakeanelastogramusingtheprocessingmethodnamed empirical mode decomposition (EMD). EMDisananalytictechniquewhichdecomposesacomplicatedsignaltoacollectionofsimplesignals alledintrinsicmodefunctions(IMFs). TheideaofpaperisusingtheseIMFsinsteadofprimaryRFsignals. Toimplementthealgorithmstwodifferentdatasetsselected. Thefirstonewasdatafromasandwichstructureofnormalandcookedtissue. The second datasetconsistedofaround 180 framesacquiredfromamalignantbreasttumor. Fordisplacementestimating, twodifferentmethods, cross-correlationandwavelettransform, wereusedtooandforevaluatingthequality, twoconventionalparameters, signal-to-noiserati (SNR)andcontrast-to-noiseratio(CNR)calculatedforeachimage. ResultsshowthatinbothmethodsafterusingEMDthequalityimproves. InfirstdatasetandcrosscorrelationtechniqueCNRandSNRimproveabout 16dBand dBrespectively. Insamedatasetbyusingwavelettechnique, theparametersshow14dBand1 dBimprovementrespectively.In second dataset(breasttumordata CNRandSNRincrosscorrelationmethodimprove 18dBand 7dBandinwavelettechniqueimprove 17dBand 6dBrespectively.

Quantification of arterial elasticity and its dependency to age is considered in this paper. We use radiofrequency (RF) signals fromcarotid artery ultasonography to evaluate this dependency. Blood pressure, blood flow, and tethering to surrounding tissue are the maincauses of the motion of the carotid wall. Tracking carotid artery wall motion from a series of ultrasound B‑mode images is challengingdue to the presence of noise and variable contrast. Moreover, the process of converting RF signals into the B‑mode images causessome information to be lost. Hence, our goal is to extract the carotid wall motions and vibrations from RF signals. After extraction andremoving the wall motion by using the phased tracking method combined with continuous wavelet transform, the vibrations of carotidinner wall in different subjects in different ages are compared with each other. Empirical mode decomposition method is used forextracting the first intrinsic mode function for different subjects’ vibration and then their zero‑crossing rates are compared. The resultsƒow the vibrations of the carotid inner wall are clearly decreased by age.

Motion analysis and quality assessment of human sperm cell is of great importance for clinical applications of male infertility. Sperm tracking is quite complex due to cell collision, occlusion and missed detection. The goal of this study is simultaneous tracking of multiple human sperm cells. In the first step in this research the frame difference algorithm is used for background subtraction. There are some limitations to select an appropriate threshold value since the output accuracy is strongly dependant on the selected threshold value. To eliminate this dependency, we propose an improved nonlinear diffusion filtering in time domain. Nonlinear diffusion filtering is a smoothing and noise removing approach that can preserve edges in images.Many sperms that move with different speeds in different directions eventually coincide. For multiple tracking over time, an optimal matching strategy is introduced that is based on the optimization of a new cost function. A Hungarian search method is utilized to obtain the best matching for all possible candidates. Results show 3.24% frame based error in dataset of videos that contain more than 1 and less than 10 sperm cells. So the accuracy rate was 96.76%. These results indicate the validity of the proposed algorithm to perform multiple sperms tracking.

Planar D-bar integral equation is one of the inverse scattering solution methods for complex problems including inverse conductivity considered in applications such as Electrical impedance tomography (EIT). Recently two different methodologies are considered for the numerical solution of D-bar integrals equation, namely product integrals and multigrid. The first one involves high computational burden and the other one suffers from low convergence rate (CR). In this paper, a novel high speed moment method based using the sinc basis is introduced to solve the two-dimensional D-bar integral equation. In this method, all functions within D-bar integral equation are first expanded using the sinc basis functions. Then, the orthogonal properties of their products dissolve the integral operator of the D-bar equation and results a discrete convolution equation. That is, the new moment method leads to the equation solution without direct computation of the D-bar integral. The resulted discrete convolution equation maybe adapted to a suitable structure to be solved using fast Fourier Transform. This allows us to reduce the order of computational complexity to as low as O (N2 log N). Simulation results on solving D-bar equations arising in EIT problem show that the proposed method is accurate with an ultra-linear CR.

Laparoscopy or minimally invasive surgery is a surgical procedure in which laparoscope and other surgical instruments are inserted inside body via a few small incisions. Laparoscope is used to look inside the patient''s body and records displayed images. Temporal segmentation of laparoscopic videos has many applications like detecting laparoscopic anomalies and interrupts. It is prerequisite of laparoscopic action recognition for tagging laparoscopic video clips, training to the surgeons and fast retrieval of tagged laparoscopic video clips. Temporal segmentation of videos is is done with the aim of generating homogeneous segments. In this paper, a novel approach for minimally-invasive video segmentation (MIVS) is proposed. In MIVS, several data sets are extracted from laparoscopic videos for increasing the confidence and reducing error of estimation. Each extracted data set is segmented individually with Genetic Algorithm several times after outlier removal. Each time, a different cost function is used as objective function of GA. The correlation coefficient is measured between objective values of individuals of each GA execution and their associated performance measures including detection rate, recognition rate and accuracy. Cost functions having negative correlation with all mentioned performance measures are selected as cost function of the next step segmentation which segments several data sets simultaneously exploiting Multi-objective GA. MIVS is tested on laparoscopic videos of Varicocelle and UPJO surgeries collected from HASHEMINEZHAD Kidney Center. Experimental results show that MIVS can segment laparoscopic videos with accuracy of 94.89%. MIVS outperforms previous presented segmentation methods in segmenting minimally-invasive surgical videos.

Improving the quality of medical images at pre and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations (PDEs)-based models have become a powerful and well known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.