A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

Tasel S. F. , Mumcuoglu E. U. , Hassanpour R. Z. , Perkins G.

JOURNAL OF STRUCTURAL BIOLOGY, vol.194, pp.253-271, 2016 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 194
  • Publication Date: 2016
  • Doi Number: 10.1016/j.jsb.2016.03.002
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.253-271
  • Keywords: Image processing, Electron tomography, Curve fitting, Active contour, Mitochondrion, CELL-CENTERED DATABASE, AUTOMATIC SEGMENTATION, ELECTRON-MICROSCOPY, FILTER


Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondria] function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14 nm respectively. (C) 2016 Elsevier Inc. All rights reserved.