3D boundary extraction of confocal cellular images using higher order statistics.
In recent years, cell biologists have benefited greatly from using confocal microscopy to study intracellular organelles. For high-level image analysis, 3D boundary extraction of cell structure is a preliminary requisite in confocal cellular imaging. To detect the object boundaries, most investigators have used gradient/Laplacian operator as a principal tool. In this paper we propose a higher order statistics (HOS) based boundary extraction algorithm for confocal cellular image data set using kurtosis. After the initial pre-processing, kurtosis boundary map is estimated locally for the entire volume using a cubic sliding window and subsequently the noisy kurtosis value is removed by thresholding. Voxels having positive kurtosis value with zero-crossing on its surface are then identified as boundary voxels. Typically used in signal processing, kurtosis for 3D cellular image processing is a novel application of HOS. Its reliable and robust nature of computing makes it very suitable for volumetric cellular boundary extraction.
Indhumathi
C
C
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
Cai
Y Y
YY
Guan
Y Q
YQ
Opas
M
M
D000818:Animals; D001921:Brain / Q000648:ultrastructure*; D002467:Cell Nucleus / Q000648:ultrastructure*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D018613:Microscopy, Confocal / Q000379:methods*; D009474:Neurons / Q000648:ultrastructure*