Journal of Medical Signals and Sensors
Volume:2 Issue: 3, Jul-Sep 2012

The quantitative analysis of cardiac motions in echocardiography images is a noteworthy issue in processing of these images. Cardiac motions can be estimated by Optical Flow (OF) computation in different regions of image which is based on the assumption that the intensity of a moving pattern remains constant in consecutive frames. However, in echocardiographic sequences this assumption may be violated because of unique specifications of ultrasound. There are some methods applying the brightness variation effect in OF. Almost all of them have presented a mathematical brightness variation model globally in the images. Nevertheless, there is not a brightness variation model for echocardiographic images in these methods. Therefore, we are looking for a method to apply brightness variations locally in different regions of the image. In this study, we proposed a method to modify usual OF technique by considering intensity variation. To evaluate this method, we implement two other OF-based methods, one usual OF method and a modified OF method applying brightness variation as a multiplier and an offset (GDIM); and compare them with ours. These algorithms and ours were implemented on real echocardiograms. Our method resulted in more accurate estimations than two others. At last, we compared our method with expert''s point of view and observed that three distance metrics between them was appropriately smaller than other methods. The Haussdorff distance between the estimated curve defined by the proposed method and the expert defined curve, is 4.81 pixels less than this distance for Lukas-Kanade and 2.28 pixels less than GDIM.

We present a novel and e±cient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain-computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coe±cients of discrete Daubechies 4 wavelet, and for selecting the effective EEG channels, we utilize an improved binary particleswarm optimization algorithm (IBSPO) together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classiffication algorithm based on Bayesian linear discriminant analysis (BLDA). We also tested our proposed scheme on Hoffmann''s dataset for eight subjects, and achieved similar results.

For superficial lesions, the electrons may be used for radiation therapy. The high energy photons and electrons are produced by a Linear accelerator (Linac). Many of electron fields need the shielding of normal or critical organs. The electron shields are usually lead slabs with few millimeter thickness which should be placed near the skin, less than 1 centimeter away from skin. In the inspection of patients setting in a clinic by a physicist, it was noted that, in some cases the technician places the shields far away from skin in the way that the shadow of the field still matches the shielded area. This is due to a conceptual mistake in which one assumes that electrons travel in a straight line and matching the shadow of lead slab is enough for the shielding. This project is about Monte Carlo simulation of this case and dosimetry in which the excess dose to the tissue under the shield is calculated. In this study BEAMnrc and DOSXYZnrc is used for simulation of the Linac and the electron shields. The water phantom as well as the Linac head (NEPTON Linac) is simulated in the electron mode. The simulation is performed in 3 various cases in which the lead shield is placed in distances of 1, 20, 40 centimeters from the surface of the phantom. In all cases, the edge of the shield is matched with the light field, so the shields get smaller as they move from the surface because of the divergence of the light field. The simulations were done in two energies, 6 and 13 MeV. The experiments also were done with EDR2 film dosimetry and the simulation results were validated using the experimental results.In all cases the dose under the shield were normalized to the dose in the center. The dose of the normal organ under the shield was around 10 %, 30%, 35 % with respect to the center for shield distances of 1, 20, 40 cm respectively. So there is a considerable increasing of the dose due to the distanced shielding. In this work exact amount of the dose from this mistake is calculated and simulated.

Radioiodine therapy is an effective method for treating thyroid cancer carcinoma, but it has some affects on normal tissues, hence dosimetry of vital organs are important to weigh the risks and benefits of this method. The aim of this study is to measure the absorbed doses of important organs by MCNP (Monte Carlo N Particle) simulation and comparing the results of different methods of dosimetry by performing t-paired test. To calculate the absorbed dose of thyroid, sternum and cervical vertebra using MCNP code, *F8 tally was used. Organs were simulated by using a neck phantom and MIRD (Medical Internal Radiation Dosimetry) method. Finally, the results of MCNP, MIRD and TLD measurements were compared by SPSS software. The absorbed dose obtained by Monte carlo simulations for 100, 150 and 175 mci administered 131I was found to be 388.0, 427.9 and 444.8 cGy for thyroid, 208.7, 230.1 and 239.3 cGy for sternum and 272.1, 299.9 and 312.1 cGy for cervical vertebra. The results of paired t-test was 0.24 for comparing TLD dosimetry and MIRD calculation, 0.80 for MCNP simulation and MIRD, and 0.19 for TLD and MCNP. The results showed no significant differences among three methods of Monte Carlo simulations, MIRD calculation and direct experimental dosimetry using TLD.

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination & paraclinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnea, Q Wave, ST Elevation, ST Depression and Tinversion. These data were collected from 303 random visitor of Tehran’s Shaheed Rajaei Cardiovascular, Medical and Research Center, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour algorithm (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 5.51% for Left Anterior Descending (LAD), 63.76 9.73% for Left Circumflex (LCX) and 68.33 6.90% for Right Coronary Artery (RCA). The effective features of stenosis of each vessel were found too.

Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation (MDMC) coupled with a peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis (LDA) to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose ovarian cancer patients from non-cancer control group with accuracy of 100%, sensitivity of 100%, and specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increase reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power.

In this paper we present a method related to extracting white blood cells (WBCs) from blood microscopic figures and recognizing them and counting each kind of WBCs. In this method, first we extract the white blood cells from other blood cells by RGB color system''s help. In continuance, by using the features of each kind of globules and their color scheme, we extract a normalized feature vector, and for classifying, we send it to a complex-valued back-propagation neural network. And at last, we send the results to the output in the shape of the quantity of each of white blood cells.

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimate and then removed. An adaptive neuro-fuzzy interference system which has a nonlinear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram (ECG) signal. Adaptive neural combined with Fuzzy System to cunstruct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc is determined by learning data. At the end simulated experimental results are presented for proper validation.