6. 3D Image Segmentation
Image segmentation is a way to electronically dissect significant biological components
from a 3D map of a macromolecule, and thereby obtain a clearer view into the
macromolecules architectural organization. For instance, it is often helpful to segment
an asymmetric local subunit (structure unit) out of an icosahedral virus such that further
structural interpretation can be conducted only on the structure unit instead of the entire
map without loss of any structural information . Automated segmentation is still recognized
as one of the challenge problems in image processing. Commonly used semi-automatic methods
include segmentation based on edge detection, region growing and/or region merging, active
curve/surface motion and model based segmentation (see for example [1,2]). Publications [3,4]
present steps towards an automatic approach for asymmetric subunit detection and
segmentation of 3D maps of icosahedral viruses. The approach is a multi-seeded, multi-object
boundary tracking variant of the well-known fast marching method of Malladi, Sethian. The
traditional fast marching method are designed for a single object boundary segmentation.
In order to segment multiple targets, such as 60-component virus capsids or a 3-component
molecular trimeric subunit, one has to choose a seed for each of the components. However,
assigning only one seed to each component may cause appropriate boundary detection problems, as
demonstrated in , and hence a multi-seeded initialization scheme becomes necessary. There
are several steps to achieve automatic structure unit segmentation for icosahedral virus
maps: (i) Decide the global (icosahedral) symmetry; (ii) Segment the capsid layers; (iii)
Detect the local symmetries; (iv) Segment the subunits based on the global/local symmetries; (v)
If needed, segment the monomers from the segmented subunits (trimers, pentons, hexons, etc.).
The following pictures show the segmentation results of the Rice Dwarf Virus (RDV) and the
Figure 1 Segmentation of rice dwarf virus (RDV) at resolution 6.8Å. (A) Original map. (B) Segmented
outer capsid layer. (C) Symmetry axes (both global icosahedra symmetry
and local 6-fold and 3-fold symmetries). (D) Segmented asymmetric units (60 copies in total).
Each subunit consists of trimers. (E) Segmentation into trimers (260 in total). There are
five types of trimers colored differently. (F) One of the segmented trimers and the segmentation into monomers.
Figure 2 Segmentation of Bacteriophage P22 after the capsid maturation at resolution 9.5Å.
(A) Original map. (B) Segmented capsid layer. (C) Symmetry axes (both global icosahedra
symmetry and local 6-fold symmetries). (D) Segmented asymmetric units (60 copies in total). Each subunit
consists of one hexon and penton. Since no local symmetry is enforced, the
hexon does not appear to have exactly 6-fold symmetry. (E) Segmentation into pentons (12 in total
and colored in red) and hexons (60 in total and colored randomly). (F) Segmentation into
pentons and hexons (looking from inside). (G) One of the segmented pentons (left) and the segmentation
(middle) into monomers (right). (H) One of the segmented hexons (left) and the
segmentation (middle) into monomers (right).
- Zeyun Yu and Chandrajit Bajaj, "Image Segmentation Using Gradient Vector Diffusion and Region Merging",
Proceedings of the 16th International Conference on Pattern Recognition (ICPR'02), Vol. 2, pp. 941-944, Quebec, Canada, August 2002.
- Zeyun Yu and Chandrajit Bajaj, "Normalized Gradient Vector Diffusion and Image Segmentation", Proceedings
of the 7th European Conference on Computer Vision (ECCV'02), Lecture
Notes in Computer Science, Vol. 2352, pp. 517-530, Copenhagen, Denmark, May 2002.
- Chandrajit Bajaj, Zeyun Yu, and Manfred Auer, "Volumetric Feature Extraction and Visualization of Tomographic
Molecular Imaging", Journal of Structural Biology, Vol. 144, No. 1-2, pp. 132-143, October 2003.
- Zeyun Yu and Chandrajit Bajaj, "Automatic Ultra-structure Segmentation of Reconstructed Cryo-EM Maps of Icosahedral Viruses", IEEE Transactions on Image
Processing: Special Issue on Molecular and Cellular Bioimaging, Vol. 14, No. 9, pp. 1324-1337, September 2005.
- Chandrajit Bajaj and Zeyun Yu, "Geometric Processing of Reconstructed 3D Maps of Molecular Complexes", in Handbook of Computational Molecular Biology, Edited by
S. Aluru, Chapman & Hall/CRC Press, Computer and Information Science Series, October 2005.