A Method to Construct Flat Maps of the Brain's Surface and its Application.

Vrije Universiteit Brussel, Bruxelles, Brussels Capital, Belgium
International Journal of Pattern Recognition and Artificial Intelligence (Impact Factor: 0.67). 08/2006; 20(05):679-710. DOI: 10.1142/S0218001406004879
Source: DBLP


This paper describes a surface flattening technique, which has been developed in particular to obtain a complete view of the cortical surface of the brain. However, the method is able to produce an overall planar view of any anatomical or real-life object, provided it is topologically compatible with the sphere (i.e. genus 0). It computes the shading of the original surface for rays casted from a nearby surrounding surface and unfolds this surface in a 2D plane, without introducing major distortions. The flat image consisting of the mapped shading results has the advantage that the sulci (i.e. the grooves characterizing the superficial brain geometry) of the cortical surface of the brain can be followed in their entirety, which facilitates the study and the recognition of their patterns. The new visualization method is integrated into a versatile medical image analysis environment. A first study to assess its usefulness has been accomplished and is also reported in this paper.

3 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.
    No preview · Article · Nov 2008 · International Journal of Pattern Recognition and Artificial Intelligence
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Since dual energy X-ray absorptiometry (DXA) cannot distinguish between different adipose tissue (AT) deposits, it remains unclear how DXA-derived body composition variables relate to anatomical tissue (sub)compartments. The aim of the present study was to compare and relate regional DXA variables with absolute tissue masses obtained by computer tomography (CT) scanning of the lower limbs in elderly persons. Eleven well-preserved white Caucasian adults (seven male and four female cadavers) with a median age of 79.0 years (ranging from 68 to 96 years) were fully scanned with DXA and CT. Separate densities of skin tissue, AT, muscle tissue and bone were obtained by hydrostatic weighing. The leg DXA-variables were significantly related (rho-values between 0.60 and 0.98, P<0.01) to CT-derived tissue counterparts, but showed significant systematic differences except for subcutaneous AT mass (P=0.773). After controlling for other AT depots, fat as measured by DXA (fatDXA) related only to intermuscular AT (rho=0.82, P<0.01) in males and to subcutaneous AT (rho=0.84, P<0.05) in females. Although significantly interrelated, DXA and CT variables should not be used interchangeably since they have different quantitative and physiological significance. Our results suggest that fatDXA represents different parts of AT depots in elderly men and women. Since DXA is not appropriate for assessing tissue variability cautious clinical interpretation is warranted.
    Full-text · Article · Jul 2013 · Experimental gerontology