International Journal for Numerical Methods in Biomedical Engineering

Cover image for Vol. 29 Issue 9

Special Issue: COMPUTATIONAL METHODS FOR BIOMEDICAL IMAGE PROCESSING AND ANALYSIS

September 2013

Volume 29, Issue 9

Pages 885–1013

Issue edited by: João Manuel R. S. Tavares, Renato Natal Jorge

  1. Editorial- Computational Methods for Biomedical Image Processing and Analysis

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    2. Editorial- Computational Methods for Biomedical Image Processing and Analysis
    3. Computational Methods for Biomedical Image Processing and Analysis
    4. Note
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  2. Computational Methods for Biomedical Image Processing and Analysis

    1. Top of page
    2. Editorial- Computational Methods for Biomedical Image Processing and Analysis
    3. Computational Methods for Biomedical Image Processing and Analysis
    4. Note
    1. Automatic MRI 2D brain segmentation using graph searching technique (pages 887–904)

      Valentina Pedoia and Elisabetta Binaghi

      Version of Record online: 27 JUN 2012 | DOI: 10.1002/cnm.2498

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      In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment.

    2. Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines (pages 905–915)

      Jean-Baptiste Fiot, Laurent D. Cohen, Parnesh Raniga and Jurgen Fripp

      Version of Record online: 10 JAN 2013 | DOI: 10.1002/cnm.2537

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      This paper presents a white matter hyper-intensity segmentation method that combines advanced pre-processing, tissue-based feature selection and supports vector machines classification. The proposed pipeline has been validated on a database of 125 patients with four magnetic resonance image modalities. The classification performance has been evaluated with regard to the relative input of each modality, the feature type, the impact of feature selection, and compared with other supervised algorithms.

    3. Modeling and biomechanical analysis of craniosynostosis correction with the use of finite element method (pages 916–925)

      Wojciech Wolański, Dawid Larysz, Marek Gzik and Edyta Kawlewska

      Version of Record online: 2 AUG 2012 | DOI: 10.1002/cnm.2506

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      This paper presents the engineering preoperative planning of skull shape correction. The MIMICS software was used to calculate the three-dimensional geometrical model and to plan the incisions, and the ANSYS software was used to perform the finite element analysis and to find the optimal variant of correction. This procedure of planning can be applied in both classic and endoscopic skull surgeries in diagnosed craniosynostosis (trigonocephaly, scaphocephaly, plagiocephaly, or brachycephaly).

    4. Engineering-aided treatment of chest deformities to improve the process of breathing (pages 926–937)

      Bożena Gzik-Zroska, Wojciech Wolański and Marek Gzik

      Version of Record online: 19 JUN 2013 | DOI: 10.1002/cnm.2563

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      In this paper, the developed method of engineering support in the surgical correction of funnel chest is presented. The present medical techniques of correction (e.g., the Nuss method) do not consist of the selection of implant customized for individual patient. In described method, the optimization of construction features of implants was applied. The obtained implant is different from available solution, so it is necessary to introduce the custom-design production of these elements.

    5. Automated spine and vertebrae detection in CT images using object-based image analysis (pages 938–963)

      M. Schwier, T. Chitiboi, T. Hülnhagen and H.K. Hahn

      Version of Record online: 14 AUG 2013 | DOI: 10.1002/cnm.2582

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      Object-based image analysis is a new approach towards semantic region-based processing of images that allows effective integration of reasoning processes and contextual concepts into the recognition method. We applied this technique to the task of spine and vertebrae detection in CT images to show how region-based features, contextual information, and domain knowledge can be used effectively in the analysis process. Our results show a detection rate for vertebral bodies of 96% and a precision of 99%.

    6. STFT or CWT for the detection of Doppler ultrasound embolic signals (pages 964–976)

      Ivo B. Gonçalves, Ana Leiria and M. M. M. Moura

      Version of Record online: 10 APR 2013 | DOI: 10.1002/cnm.2546

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      Short-time Fourier transform (STFT) and different configurations of continuous wavelet transform (CWT) were tested for detection and localization embolic events. Simulated Doppler ultrasound blood flow signals of middle cerebral artery added with embolic signals and four different locations of emboli in the cardiac cycle were tested. Compared with mother wavelets for CWT analysis were Morlet, Mexican hat, Meyer, Gaussian (order 4) and Daubechies (orders 4 and 8), and the thresholds for detection (equated in terms of false positive, false negative and sensitivity) were 2 and 3.5 dB for the CWT and STFT, respectively. The results indicate that although the STFT allows accurately detecting emboli, better time localization can be achieved with the CWT. Among the CWT, the current best overall results were obtained with Mexican hat mother wavelet, with optimal results for sensitivity (100% detection rate) for nearly all emboli power values studied.

    7. Automatic tracking of labeled red blood cells in microchannels (pages 977–987)

      Diana Pinho, Rui Lima, Ana I. Pereira and Fernando Gayubo

      Version of Record online: 12 NOV 2012 | DOI: 10.1002/cnm.2526

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      The current study proposes an automatic method for segmentation and tracking of red blood cells flowing through a 100- μm glass capillary. The original images were obtained by means of a confocal system and then processed in MATLAB using the Image Processing Toolbox. The measurements obtained with the proposed automatic method were compared with the results determined by a manual tracking method. The comparison was performed by using linear regressions and Bland-Altman analysis. The results showed a good agreement between the two methods.

    8. Monte Carlo simulation of PET images for injection dose optimization (pages 988–999)

      Jiří Boldyš, Jiří Dvořák, Magdaléna Skopalová and Otakar Bělohlávek

      Version of Record online: 26 DEC 2012 | DOI: 10.1002/cnm.2527

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      To achieve constant quality of positron emission tomography images for different patients, we have derived curves recommending the amount of injected activity based on body parameters. These curves show rather convex tendency on the contrary to today's linear or sublinear standard recommendation. This indicates there is opportunity to optimize the current recommendation with the aim to standardize the diagnostic process.

    9. Time-frequency analysis methods for detecting effects of diabetic neuropathy (pages 1000–1010)

      H. A. Weiderpass, C. G. F. Pachi, J. F. Yamamoto, A. Hamamoto, A. N. Onodera and I. C. N. Sacco

      Version of Record online: 26 APR 2013 | DOI: 10.1002/cnm.2545

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      This study compares distinct time-frequency analysis methods for investigating the electromyographic activity of the thigh and calf muscles during gait among non-diabetic subjects and diabetic neuropathic patients. It also attempts to verify, by adaptive optimal kernel and discrete wavelet transform, whether there are electromyographic alterations related to diabetic neuropathy in the lower limb muscles during gait. The results show that diabetics might not keep up with the mechanical demands of walking by changing muscle fibre recruitment strategies, as seen in the control group.

  3. Note

    1. Top of page
    2. Editorial- Computational Methods for Biomedical Image Processing and Analysis
    3. Computational Methods for Biomedical Image Processing and Analysis
    4. Note
    1. NOTE (pages 1011–1013)

      Version of Record online: 6 SEP 2013 | DOI: 10.1002/cnm.2599

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