Computer Methods and Programs in Biomedicine, Volume 79

Volume 79, Number 1, July 2005

• Jouni M. Mykkänen, Jussi Tohka, Jouni Luoma, Ulla Ruotsalainen:
  Automatic extraction of brain surface and mid-sagittal plane from PET images applying deformable models. 1-17
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• Jinn-Yi Yeh, J. C. Fu, C. C. Wu, H. M. Lin, J. W. Chai:
  Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images. 19-29
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• Aristófanes C. Silva, Paulo Cezar Pinto Carvalho, Marcelo Gattass:
  Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images. 31-38
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• Josmar Mazucheli, Emílio Augusto Coelho Barros, Jorge Alberto Achcar:
  Bootstrap confidence intervals for the mode of the hazard function. 39-47
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• Akihiro Takeuchi, Minoru Hirose, Atsushi Hamada, Noriaki Ikeda:
  Simulation system of arrhythmia using ActiveX control. 49-57
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• Wenjin Chen, Peter Meer, Bogdan Georgescu, Wei He, Lauri A. Goodell, David J. Foran:
  Image mining for investigative pathology using optimized feature extraction and data fusion. 59-72
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• Yizhou Zheng, Min Zhao:
  Modified minimal model using a single-step fitting process for the intravenous glucose tolerance test in Type 2 diabetes and healthy humans. 73-79
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• Andreas Faldum, Klaus Pommerening:
  An optimal code for patient identifiers. 81-88
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• Angus M. Brown:
  A new software for carrying out one-way ANOVA post hoc tests. 89-95
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Volume 79, Number 2, August 2005

• Ryan George Fraser, Jocelyn Armarego, Kanagasingam Yogesan:
  The reengineering of a software system for glaucoma analysis. 97-109
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• Luis Alberto Vieira de Carvalho, Odemir Martinez Bruno:
  Spatial and frequency domain techniques for segmentation of Placido images and accuracy implications for videokeratography. 111-119
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• John F. LaDisa Jr., Lars E. Olson, Kristina M. Ropella, Robert C. Molthen, Steven T. Haworth, Judy R. Kersten, David C. Warltier, Paul S. Pagel:
  Microfocal X-ray computed tomography post-processing operations for optimizing reconstruction volumes of stented arteries during 3D computational fluid dynamics modeling. 121-134
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• Tomasz Arodz, Marcin Kurdziel, Erik O. D. Sevre, David A. Yuen:
  Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms. 135-149
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• Niina Päivinen, Seppo Lammi, Asla Pitkänen, Jari Nissinen, Markku Penttonen, Tapio Grönfors:
  Epileptic seizure detection: A nonlinear viewpoint. 151-159
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• Federico Ambrogi, Elia Biganzoli, Patrizia Boracchi:
  Multiple correspondence analysis in S-PLUS. 161-167
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• Charles Harless, Joseph J. DiStefano III:
  Automated expert multiexponential biomodeling interactively over the Internet. 169-178
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• G. K. Balasubramani, Stephen R. Wisniewski, Hongwei Zhang, Heather F. Eng:
  Development of an efficient SAS^® macro to perform permutation tests for two independent samples. 179-187
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Volume 79, Number 3, September 2005

• Xiao Hu, Zhizhong Wang, Xiaomei Ren:
  Classification of surface EMG signal using relative wavelet packet energy. 189-195
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• Rossana Dell'Anna, Francesca Demichelis, Mattia Barbareschi, Andrea Sboner:
  An automated procedure to properly handle digital images in large scale Tissue Microarray experiments. 197-208
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• Mohammad-Reza Siadat, Hamid Soltanian-Zadeh, Farshad Fotouhi, Kost V. Elisevich:
  Content-based image database system for epilepsy. 209-226
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• Alberto Leardini, Luca Astolfi, Silvia Fantozzi, Marco Viceconti, M. G. Benedetti, Fabio Catani:
  Advanced multimodal visualisation of clinical gait and fluoroscopy analyses in the assessment of total knee replacement. 227-240
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• Lars Lindbom, Pontus Pihlgren, E. Niclas Jonsson:
  PsN-Toolkit - A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. 241-257
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• J. W. T. Yates, M. J. Chappell, J. W. Gardner, C. S. Dow, C. Dowson, A. Hamood, F. Bolt, L. Beeby:
  Data reduction in headspace analysis of blood and urine samples for robust bacterial identification. 259-271
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• Fatimah Ibrahim, Mohd Nasir Taib, Wan Abu Bakar Wan Abas, Chan Chong Guan, Saadiah Sulaiman:
  A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN). 273-281
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