Journal of Medical Signals and Sensors
Volume:4 Issue: 3, Jul-Sep 2014

In this paper, we propose and investigate distribution of intravascular and extravascular extracellular volume fractions (DIEEF) as a noninvasive biomarker for neovascularization assessment by dynamic contrast‑enhanced magnetic resonance imaging (DCE‑MRI). A generalized two‑compartment exchange model (G2CXM) that uniformly includes the Patlak model, Tofts model, extended Tofts model, and recent two‑compartment exchange model as special instances is first presented. Based on the total area under curve of the G2CXM a method of DIEEF estimation without knowing the artery input function is proposed. The mean square error of DIEEF estimate in the presence of noise and with incomplete DCE‑MRI data is analyzed. Simulation results demonstrate that DIEEF estimate is accurate when signal to noise ratio is only 5 dB in both cases of tracer infusion and bolus injection, and slightly favors the bolus injection. Tested on a model of atherosclerotic rabbits, the DIEEF of aorta plaques is positively correlated with the histological neovessel count with correlation coefficient of 0.940 and P = 0.017, and outperforms six semiquantitative parameters in the literature. DIEEF might be useful as a biomarker for noninvasive neovascularization assessment by DCE‑MRI.

To monitor the patient behavior, data are collected from patient’s body by a medical monitoring device so as to calculate the output using embedded software. Incorrect calculations may endanger the patient’s life if the software fails to meet the patient’s requirements. Accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from the patient’s body are fuzzy. Some methods have already dealt with monitoring the medical monitoring devices; however, model based monitoring fuzzy computations of such devices have been addressed less. The present paper aims to present synthesizing a fuzzy Petri-net (FPN) model to verify behavior of a sample medical monitoring device called continuous infusion insulin (INS) because Petri-net (PN) is one of the formal and visual methods to verify the software’s behavior. The device is worn by the diabetic patients and then the software calculates the INS dose and makes a decision for injection. The input and output of the infusion INS software are not crisp in the real world; therefore, we present them in fuzzy variables. Afterwards, we use FPN instead of clear PN to model the fuzzy variables. The paper follows three steps to synthesize an FPN to deal with verification of the infusion INS device: (1) Definition of fuzzy variables, (2) definition of fuzzy rules and (3) design of the FPN model to verify the software behavior.

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time‑consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter‑observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K‑nearest neighbor (KNN) classifier using EEG signals during emotional audio‑visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg’s method) based on Levinson‑Durbin’s recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10‑15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively.

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high‑resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time‑consuming. These challenges have led to the development of the computer‑aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast‑enhanced (DCE)‑MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE‑MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

Assessment of cardiac right‑ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right‑ventricle functional parameters from cardiac MRI images contains segmentation of right‑ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right‑ventricle in short axis MRI images. After segmentation of right‑ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right‑ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root‑mean‑square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right‑ventricle led us to use a shape prior based method and this work can develop by four‑dimensional processing for determining the first ventricular slices.

Distribution of retinal blood vessels (RBVs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. Therefore, detection of the exact location of RBVs is very important for Ophthalmologists. One of the frequently used techniques for extraction of these vessels is region growing-based Segmentation. In this paper, we propose a new region growing (RG) technique for RBVs extraction, called cellular automata‑based segmentation. RG techniques often require manually seed point selection, that is, human intervention. However, due to the complex structure of vessels in retinal images, manual tracking of them is very difficult. Therefore, to make our proposed technique full automatic, we use an automatic seed point selection method. The proposed RG technique was tested on Digital Retinal Images for Vessel Extraction database for three different initial seed sets and evaluated against the manual segmentation of retinal images available at this database. Three quantitative criteria including accuracy, true positive rate and false positive rate, were considered to evaluate this method. The visual scrutiny of the segmentation results and the quantitative criteria show that, using cellular automata for extracting the blood vessels is promising. However, the important point at here is that the correct initial seeds have an effective role on the final results of segmentation.

Gamma camera is an important apparatus in nuclear medicine imaging. Its detection part is consists of a scintillation detector with aheavy collimator. Substitution of semiconductor detectors instead of scintillator in these cameras has been effectively studied. In this study, it is aimed to introduce a new design of P-N semiconductor detector array for nuclear medicine imaging. A P-N semiconductor detector composed of N-SnO:F, and P‑NiO:Li, has been introduced through simulating with MCNPX monte carlo codes. Its sensitivitywith different factors such as thickness, dimension, and direction of emission photons were investigated. It is then used to configure anew design of an array in one‑dimension and study its spatial resolution for nuclear medicine imaging. One‑dimension array with 39detectors was simulated to measure a predefined linear distribution of Tc2 activity and its spatial resolution. The activity distributionwas calculated from detector responses through mathematical linear optimization using LINPROG code on MATLAB software. Threedifferent configurations of one‑dimension detector array, horizontal, vertical one sided, and vertical double‑sided were simulated. Inall of these configurations, the energy windows of the photopeak were ± 1%. The results show that the detector response increaseswith an increase of dimension and thickness of the detector with the highest sensitivity for emission photons 15-30° above the surface.Horizontal configuration array of detectors is not suitable for imaging of line activity sources. The measured activity distribution withvertical configuration array, double‑side detectors, has no similarity with emission sources and hence is not suitable for imagingpurposes. Measured activity distribution using vertical configuration array, single side detectors has a good similarity with sources.Therefore, it could be introduced as a suitable configuration for nuclear medicine imaging. It has been shown that using semiconductorP‑N detectors such as P‑NiO:Li, N‑SnO299_m:F for gamma detection could be possibly applicable for design of a one dimension arrayconfiguration with suitable spatial resolution of 2.7 mm for nuclear medicine imaging.