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Computational and Mathematical Methods in Medicine

Yiran Wang, Zhifeng Chen, Jing Wang, Lixia Yuan, Ling Xia, Feng Liu
The k-t principal component analysis (k-t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k-t PCA that combines the k-t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k-t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k-t space from the original reconstructed k-t space...
2017: Computational and Mathematical Methods in Medicine
Konstantinos Bromis, Kostakis Gkiatis, Irene Karanasiou, George Matsopoulos, Eustratios Karavasilis, Matilda Papathanasiou, Efstathios Efstathopoulos, Nikolaos Kelekis, Vasileios Kouloulias
Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN)...
2017: Computational and Mathematical Methods in Medicine
Stefanie Friedrichs, Juliane Manitz, Patricia Burger, Christopher I Amos, Angela Risch, Jenny Chang-Claude, Heinz-Erich Wichmann, Thomas Kneib, Heike Bickeböller, Benjamin Hofner
The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm...
2017: Computational and Mathematical Methods in Medicine
J S Estepa Jiménez, M Díaz Lagos, S A Martinez-Ovalle
Different types the spectrum of photons were studied; they were emitted from the flattening filter of a LINAC Varian 2100 C/D that operates at 15 MV. The simplified geometry of the LINAC head was calculated using the MCNPX code based on the studies of the materials of the flattening filter, namely, SST, W, Pb, Fe, Ta, Al, and Cu. These materials were replaced in the flattening filter to calculate the photon spectra at the output of this device to obtain the spectrum that makes an impact with the patient. The different spectra obtained were analyzed and compared to the emission from the original spectra configuration of the LINAC, which uses material W...
2017: Computational and Mathematical Methods in Medicine
Anqi Miao, Jian Zhang, Tongqian Zhang, B G Sampath Aruna Pradeep
A stochastic SIR model with vertical transmission and vaccination is proposed and investigated in this paper. The threshold dynamics are explored when the noise is small. The conditions for the extinction or persistence of infectious diseases are deduced. Our results show that large noise can lead to the extinction of infectious diseases which is conducive to epidemic diseases control.
2017: Computational and Mathematical Methods in Medicine
Bjarni V Halldorsson, Aron Hjalti Bjornsson, Haukur Tyr Gudmundsson, Elvar Orn Birgisson, Bjorn Runar Ludviksson, Bjorn Gudbjornsson
[This corrects the article DOI: 10.1155/2015/189769.].
2017: Computational and Mathematical Methods in Medicine
Karina Gutiérrez-Fragoso, Héctor Gabriel Acosta-Mesa, Nicandro Cruz-Ramírez, Rodolfo Hernández-Jiménez
Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4...
2017: Computational and Mathematical Methods in Medicine
Jinyu Cong, Benzheng Wei, Yunlong He, Yilong Yin, Yuanjie Zheng
Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis...
2017: Computational and Mathematical Methods in Medicine
Yuanfa Wang, Zunchao Li, Lichen Feng, Chuang Zheng, Wenhao Zhang
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector...
2017: Computational and Mathematical Methods in Medicine
Nan Jia, Xiaohui Chen, Liang Yu, Ruomei Wang, Kaixing Yang, Xiaonan Luo
Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality...
2017: Computational and Mathematical Methods in Medicine
O N Shevtsova, V K Shevtsova
The proposed model describes in a quality way the process of tumor-imaging radiopharmaceutical (99m)Tc-MIBI distribution with taking into account radiopharmaceutical accumulation, elimination, and radioactive decay. The dependencies of concentration versus the time are analyzed. The model can be easily tested by the concentration data of the radioactive pharmaceuticals in the blood measured at early time point and late time point of the scanning, and the obtained data can be used for determination of the washout rate coefficient which is one of the existing oncology diagnostics methods...
2017: Computational and Mathematical Methods in Medicine
Lu Bing, Wei Wang
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag...
2017: Computational and Mathematical Methods in Medicine
Guoqing Wang, Jun Wang
Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER)...
2017: Computational and Mathematical Methods in Medicine
A Golubev
In many cases relevant to biomedicine, a variable time, which features a certain distribution, is required for objects of interest to pass from an initial to an intermediate state, out of which they exit at random to a final state. In such cases, the distribution of variable times between exiting the initial and entering the final state must conform to the convolution of the first distribution and a negative exponential distribution. A common example is the exponentially modified Gaussian (EMG), which is widely used in chromatography for peak analysis and is long known as ex-Gaussian in psychophysiology, where it is applied to times from stimulus to response...
2017: Computational and Mathematical Methods in Medicine
Jing Wang, Zhifeng Chen, Yiran Wang, Lixia Yuan, Ling Xia
Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. In this work, geometric-decomposition coil compression (GCC) is applied to radial sampling (both linear-angle and golden-angle patterns are discussed) for better compression...
2017: Computational and Mathematical Methods in Medicine
Kyung-Wuk Kim, Young Ho Choi, Seung Bae Lee, Yasutaka Baba, Hyoung-Ho Kim, Sang-Ho Suh
The ureter provides a way for urine to flow from the kidney to the bladder. Peristalsis in the ureter partially forces the urine flow, along with hydrostatic pressure. Ureteral diseases and a double J stent, which is commonly inserted in a ureteral stenosis or occlusion, disturb normal peristalsis. Ineffective or no peristalsis could make the contour of the ureter a tube, a funnel, or a combination of the two. In this study, we investigated urine flow in the abnormal situation. We made three different, curved tubular, funnel-shaped, and undulated ureter models that were based on human anatomy...
2017: Computational and Mathematical Methods in Medicine
Ilker Unal
ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum...
2017: Computational and Mathematical Methods in Medicine
Shuji Kawasaki, Dhisa Minerva, Keiko Itano, Takashi Suzuki
We consider ordinary differential equation (ODE) model for a pathway network that arises in extracellular matrix (ECM) degradation. For solving the ODEs, we propose applying the mass conservation law (MCL), together with a stoichiometry called doubling rule, to them. Then it leads to extracting new units of variables in the ODEs that can be solved explicitly, at least in principle. The simulation results for the ODE solutions show that the numerical solutions are indeed in good accord with theoretical solutions and satisfy the MALs...
2017: Computational and Mathematical Methods in Medicine
Sheng-Cheng Huang, Hao-Yu Jan, Tieh-Cheng Fu, Wen-Chen Lin, Geng-Hong Lin, Wen-Chi Lin, Cheng-Lun Tsai, Kang-Ping Lin
Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction...
2017: Computational and Mathematical Methods in Medicine
Ivan L Milankovic, Nikola V Mijailovic, Nenad D Filipovic, Aleksandar S Peulic
Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue...
2017: Computational and Mathematical Methods in Medicine
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