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Computer Methods and Programs in Biomedicine

Chung-Ming Lo, Usman Iqbal, Yu-Chuan Jack Li
No abstract text is available yet for this article.
July 2017: Computer Methods and Programs in Biomedicine
Malihe Sabeti, Reza Boostani
Synchronous averaging over time locked single-trial of event-related potential (ERP) is known as the simplest scheme to extract P300 component. This method assumes the P300 features are invariant through the time while they are affected by factors like brain fatigue and habitation. In this study, a new scheme is proposed termed as time-varying time-lag blind source separation (TT-BSS) which is upon the second order statistics of signal to separate P300 waveform from the background electroencephalogram (EEG) while it captures the time variation of P300 component...
July 2017: Computer Methods and Programs in Biomedicine
Saeid Asgari Taghanaki, Jeremy Kawahara, Brandon Miles, Ghassan Hamarneh
BACKGROUND AND OBJECTIVE: Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error...
July 2017: Computer Methods and Programs in Biomedicine
Iraklis Varlamis, Ioannis Apostolakis, Dimitra Sifaki-Pistolla, Nilanjan Dey, Vassilios Georgoulias, Christos Lionis
BACKGROUND AND OBJECTIVE: Micro or macro-level mapping of cancer statistics is a challenging task that requires long-term planning, prospective studies and continuous monitoring of all cancer cases. The objective of the current study is to present how cancer registry data could be processed using data mining techniques in order to improve the statistical analysis outcomes. METHODS: Data were collected from the Cancer Registry of Crete in Greece (counties of Rethymno and Lasithi) for the period 1998-2004...
July 2017: Computer Methods and Programs in Biomedicine
Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj
BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system...
July 2017: Computer Methods and Programs in Biomedicine
Xiaoqi Liu, Chengliang Wang, Jianying Bai, Guobin Liao, Yanjun Zhao
BACKGROUND AND OBJECTIVE: Magnification endoscopy with narrow-band imaging (ME-NBI) has become a feasible tool for detecting diseases within the human gastrointestinal tract, and is more applied by physicians to search for pathological abnormalities with gastric cancer such as precancerous lesions, early gastric cancer and advanced cancer. In order to improve the reliability of diseases detection, there is a need for applying or proposing computer-assisted methodologies to efficiently analyze and process ME-NBI images...
July 2017: Computer Methods and Programs in Biomedicine
Chin-Chen Chang, Hong-Hao Chen, Yeun-Chung Chang, Ming-Yang Yang, Chung-Ming Lo, Wei-Chun Ko, Yee-Fan Lee, Kao-Lang Liu, Ruey-Feng Chang
BACKGROUND AND OBJECTIVE: Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images...
July 2017: Computer Methods and Programs in Biomedicine
Jin Woo Choi, Hajeong Lee, Jung Chan Lee, Saram Lee, Yon Su Kim, Hyung-Jin Yoon, Hee Chan Kim
BACKGROUND: The conventional hemodialysis (HD) schedule has been used for decades, even though new modalities have been introduced. Many reasons limit practices of frequent dialysis, such as patients' environments and unknown optimal schedules for each patient. This research provides a theoretical recommendation of HD schedule through genetic algorithm (GA). METHODS: An end-stage renal disease (ESRD) with various dialysis conditions was modeled through a classic variable-volume two-compartment kinetic model to simulate an anuric patient, and GA was implemented to search for an optimal HD schedule for each individual considering and ignoring burden consumption of each dialysis session...
July 2017: Computer Methods and Programs in Biomedicine
Andrés Cardona, Leandro Ariza-Jiménez, Diego Uribe, Johanna C Arroyave, July Galeano, Fabian M Cortés-Mancera
BACKGROUND AND OBJECTIVES: Cell imaging is a widely-employed technique to analyze multiple biological processes. Therefore, simple, accurate and quantitative tools are needed to understand cellular events. For this purpose, Bio-EdIP was developed as a user-friendly tool to quantify confluence levels using cell culture images. METHODS: The proposed algorithm combines a pre-processing step with subsequent stages that involve local processing techniques and a morphological reconstruction-based segmentation algorithm...
July 2017: Computer Methods and Programs in Biomedicine
Sandra Morales, Angela Bernabeu-Sanz, Fernando López-Mir, Pablo González, Luis Luna, Valery Naranjo
BACKGROUND AND OBJECTIVE: This paper presents BRAIM, a computer-aided diagnosis (CAD) system to help clinicians in diagnosing and treatment monitoring of brain diseases from magnetic resonance image processing. BRAIM can be used for early diagnosis of neurodegenerative diseases such as Parkinson, Alzheimer or Multiple Sclerosis and also for brain lesion diagnosis and monitoring. METHODS: The developed CAD system includes different user-friendly tools for segmenting and determining whole brain and brain structure volumes in an easy and accurate way...
July 2017: Computer Methods and Programs in Biomedicine
Ziran Peng, Guojun Wang, Huabin Jiang, Shuangwu Meng
Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces signal compression ratio. This paper studies the optimization of the EZW compression algorithm and aims to improve it. First, we used lifting wavelet transformation to process electrocardiograph (ECG) signals, focusing on the lifting algorithm. Second, we utilized the EZW compression coding algorithm, through the ECG information decomposition to determine the feature detection value...
July 2017: Computer Methods and Programs in Biomedicine
Manuel Blanco-Velasco, Rebeca Goya-Esteban, Fernando Cruz-Roldán, Arcadi García-Alberola, José Luis Rojo-Álvarez
BACKGROUND AND OBJECTIVE: T-wave alternans (TWA) is a fluctuation of the ST-T complex occurring on an every-other-beat basis of the surface electrocardiogram (ECG). It has been shown to be an informative risk stratifier for sudden cardiac death, though the lack of gold standard to benchmark detection methods has promoted the use of synthetic signals. This work proposes a novel signal model to study the performance of a TWA detection. Additionally, the methodological validation of a denoising technique based on empirical mode decomposition (EMD), which is used here along with the spectral method, is also tackled...
July 2017: Computer Methods and Programs in Biomedicine
Deepak Joshi, Aayushi Khajuria, Pradeep Joshi
BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various analysis and classification approaches using spatiotemporal gait variables have been presented earlier in classifying Parkinson's gait. In this paper, we present a wavelet transform based representation of spatiotemporal gait variables to explore the potential of such representation in the identification of Parkinson's gait. METHODS: Here, we present wavelet analysis as an alternate method and show that wavelet analysis combined with support vector machine (SVM) can produce efficient classification accuracy...
July 2017: Computer Methods and Programs in Biomedicine
Ju Long, Michael Juntao Yuan
A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator...
July 2017: Computer Methods and Programs in Biomedicine
J Dhalia Sweetlin, H Khanna Nehemiah, A Kannan
BACKGROUND AND OBJECTIVES: Computer-aided diagnosis (CAD) plays a vital role in the routine clinical activity for the detection of lung disorders using computed tomography (CT) images. It serves as a source of second opinion that radiologists may consider in order to interpret CT images. In this work, the purpose of CAD is to improve the diagnostic accuracy of pulmonary bronchitis from CT images of the lung. METHODS: Left and right lung fields are segmented using optimal thresholding from the lung CT images...
July 2017: Computer Methods and Programs in Biomedicine
Babak Sharif, Amir Homayoun Jafari
BACKGROUND AND OBJECTIVE: Epilepsy is a neurological disorder that causes recurrent and abrupt seizures which makes the patients insecure. Predicting seizures can reduce the burdens of this disorder. METHODS: A new approach in seizure prediction is presented that includes a novel technique in feature extraction from EEG. The algorithm firsts creates an embedding space from EEG time series. Then it takes samples with most of the information using an optimized and data specific Poincare plane...
July 2017: Computer Methods and Programs in Biomedicine
Nan Jiang, Yi Zhuang, Dickson K W Chiu
BACKGROUND AND OBJECTIVE: In the state-of-the-art image transmission methods, multiple large medical images are usually transmitted one by one which is very inefficient. The objective of our study is to devise an effective and efficient multiple transmission optimization scheme for medical images called Mto via analyzing the visual content of the multiple images based on the characteristics of a recourse-constraint mobile telemedicine system (MTS) and the medical images; METHODS: To better facilitate the efficient Mto processing, two enabling techniques, i...
July 2017: Computer Methods and Programs in Biomedicine
Huthaifa N Abderahman, Hilmi R Dajani, Miodrag Bolic, Voicu Z Groza
Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. Motion artifacts are considered external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibrations. In the proposed work, a new algorithmic stage is integrated in a non-invasive BP monitor. This stage suppresses the effect of the motion artifact and adjusts the pressure estimation before displaying it to users. The proposed stage is based on a 3-axis accelerometer signal, which helps in the accurate detection of the motion artifact...
July 2017: Computer Methods and Programs in Biomedicine
Shabbir Syed-Abdul, Usman Iqbal, Yu-Chuan Jack Li
No abstract text is available yet for this article.
June 2017: Computer Methods and Programs in Biomedicine
Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen Moore, Hong Liu, Bin Zheng
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups...
June 2017: Computer Methods and Programs in Biomedicine
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