Pixel-based statistical analysis by a 3D clustering approach: Application to autoradiographic images

  • Authors:

  • Weizhao Zhao;Chunyan Wu;Kai Yin;Tzay Y. Young;Myron D. Ginsberg

  • Affiliations:

  • Department of Biomedical Engineering, University of Miami, PO Box 248294, Coral Gables, FL 33124-0640, USA and Cerebral Vascular Disease Research Center, Department of Neurology, University of Mia ...;Department of Biomedical Engineering, University of Miami, PO Box 248294, Coral Gables, FL 33124-0640, USA and Cerebral Vascular Disease Research Center, Department of Neurology, University of Mia ...;Department of Electrical and Computer Engineering, University of Miami, PO Box 248294, Coral Gables, FL 33124-0640, USA and Cerebral Vascular Disease Research Center, Department of Neurology, Univ ...;Department of Electrical and Computer Engineering, University of Miami, PO Box 248294, Coral Gables, FL 33124-0640, USA;Cerebral Vascular Disease Research Center, Department of Neurology, University of Miami, Miller School of Medicine, PO Box 016960, Miami, FL 33101-6960, USA

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2006

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Abstract

Statistical analysis of medical images in experimental laboratories plays an important role in confirming scientific findings and in guiding potential clinical applications. In experimental neuroscience studies, autoradiographic images taken under differing physiological or pathological conditions from replicate animals are often compared in order to detect any significant change in glucose utilization or blood flow and to localize these changes. For these comparisons to be valid and informative, proper statistical procedures are in order. Conventional methods include statistic parametric mapping (SPM) analysis, non-parametric analysis and cluster-analysis. Each method of comparison has a specific purpose. This paper describes an approach that combines these conventional methods and presents a non-parametric statistical procedure based on cluster-analysis for localizing significant differences in autoradiographic data sets. By thresholding cluster sizes rather than pixel values to reject false positives, this approach enhances statistical power. By re-shuffling the data sets to produce the null distribution of a cluster size statistic, the test makes few assumptions as to the statistical properties of the SPM, and thus it is valid under a broad range of conditions. The designed method was tested on autoradiographic images of rats subjected to moderate traumatic brain injury (TBI). Different methods were also performed on the same data sets. Comparison among these methods shows that this method is suitable for the statistical analysis of autoradiographic images.