Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

Anderson Alvarenga De Moura Meneses, Christiano Jorge Gomes Pinheiro, Paola Rancoita, Tom Schaul, Luca Maria Gambardella, Roberto Schirru, Regina Cely Barroso, Lus Fernando De Oliveira

Research output: Contribution to journalArticlepeer-review


Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images.

Original languageEnglish
Pages (from-to)662-669
Number of pages8
JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Issue number1-3
Publication statusPublished - 2010


  • Artificial intelligence
  • Artificial neural networks
  • Histomorphometry
  • Micro-computed tomography
  • Synchrotron radiation

ASJC Scopus subject areas

  • Instrumentation
  • Nuclear and High Energy Physics


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