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

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.

journal_name

J Struct Biol

authors

Dietlmeier J,Ghita O,Duessmann H,Prehn JH,Whelan PF

doi

10.1016/j.jsb.2013.10.013

subject

Has Abstract

pub_date

2013-12-01 00:00:00

pages

401-8

issue

3

eissn

1047-8477

issn

1095-8657

pii

S1047-8477(13)00278-5

journal_volume

184

pub_type

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