Journal Article


Paul F. Whelan
Jochen H M Prehn
Heiko Duessmann
Ovidiu Ghita
Julia Dietlmeier



molecular imaging mitochondria image interpretation computer assisted sciuridae pattern recognition automated animals microscopy electron cluster analysis algorithms

Unsupervised mitochondria segmentation using recursive spectral clustering and adaptive similarity models. (2013)

Abstract The unsupervised segmentation method proposed in the current study follows the evolutional ability of human vision to extrapolate significant structures in an image. In this work we adopt the perceptual grouping strategy by selecting the spectral clustering framework, which is known to capture perceptual organization features, as well as by developing similarity models according to Gestaltic laws of visual segregation. Our proposed framework applies but is not limited to the detection of cells and organelles in microscopic images and attempts to provide an effective alternative to presently dominating manual segmentation and tissue classification practice. The main theoretical contribution of our work resides in the formulation of robust similarity models which automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions.
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Full list of authors on original publication

Paul F. Whelan, Jochen H M Prehn, Heiko Duessmann, Ovidiu Ghita, Julia Dietlmeier

Experts in our system

Paul F. Whelan
Dublin City University
Total Publications: 162
Jochen H M Prehn
Royal College of Surgeons in Ireland
Total Publications: 206
Julia Dietlmeier
Dublin City University
Total Publications: 9