Journal of Medical Signals and Sensors
Volume:6 Issue: 3, Jul-Sep 2016

There are different approaches to business model categorization.[5] In a point of view, the E-business model patterns can be categorized to content provider, direct to customer (D2C), full-service provider, intermediary, shared infrastructure, value net integrator, virtual community, and whole of enterprise.

The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HM 195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm.

Considering the nonlinear hyperelastic or viscoelastic nature of soft tissues has an important effect on modeling results. In medical applications, accounting nonlinearity begets an ill posed problem, due to absence of external force. Myocardium can be considered as a hyperelastic material, and variational approaches are proposed to estimate stiffness matrix, which take into account the linear and nonlinear properties of myocardium. By displacement estimation of some points in the four-dimensional cardiac magnetic resonance imaging series, using a similarity criterion, the elementary deformations are estimated, then using the Moore–Penrose inverse matrix approach, all point deformations are obtained. Using this process, the cardiac wall motion is quantized to mechanically determine local parameters to investigate the cardiac wall functionality. This process was implemented and tested over 10 healthy and 20 patients with myocardial infarction. In all patients, the process was able to precisely determine the affected region. The proposed approach was also compared with linear one and the results demonstrated its superiority respect to the linear model.

X-ray coronary angiography has been a gold standard in the clinical diagnosis and interventional treatment of coronary arterial diseases for decades. In angiography, a sequence of images is obtained, a few of which are sui table for physician inspection. This paper proposes an automatic algorithm for the extraction of one or more frames from an angiogram sequence, which is most sui table for diagnosis and analysis by experts or processors. The algorithm consists of two stages: In the first stage, the background and illumination in the angiogram sequence are omitted. By analyzing the histogram of the sequence, a feature is attributed to each frame. These features, determining the visibility of the vessel tree, are clustered by a fuzzy c-means method. In the second stage, the cardiac phase for each frame is specified. Using the results of both stages, the best frames in an angiogram sequence are obtained. To evaluate the proposed method, it has been tested on angiogram sequences from several patients. The results demonstrate the accuracy of the method. The performance and speed of our method indicate its usefulness in clinical applications.

Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60–40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision.

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3 mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Breathomics is the metabolomics study of exhaled air. It is a powerful emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the quantity of these compounds varies with health status, breathomics assures to deliver noninvasive diagnostic tools. Thus, the main aim of breathomics is to discover patterns of VOCs related to abnormal metabolic processes occurring in the human body. Classification systems, however, are not designed for cost-sensitive classification domains. Therefore, they do not work on the gastric carcinoma (GC) domain where the benefit of correct classification of early stages is more than that of later stages, and also the cost of wrong classification is different for all pairs of predicted and actual classes. The aim of this work is to demonstrate the basic principles for the breathomics to classify the GC, for that the determination of VOCs such as acetone, carbon disulfide, 2-propanol, ethyl alcohol, and ethyl acetate in exhaled air and stomach tissue emission for the detection of GC has been analyzed. The breath of 49 GC and 30 gastric ulcer patients were collected for the study to distinguish the normal, suspected, and positive cases using back-propagation neural network (BPN) and produced the accuracy of 93%, sensitivity of 94.38%, and specificity of 89.93%. This study carries out the comparative study of the result obtained by the single- and multi-layer cascade-forward and feed-forward BPN with different activation functions. From this study, the multilayer cascade-forward outperforms the classification of GC from normal and benign cases.

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2–M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.