Approach: 1. Segmentation and Feature Extraction

The proposed integrative analysis starts with the construction of cellular morphometric types and cellular morphometric context, followed by integrative analysis with both clinical and molecular data. Tthe constructed cellular morphometric context representations are released through the project TCGA LGG Cohort Cellular Morphometric Context Summarization.

The total number of morphometric features involved in this summarization is 15. These featurs are derived using the methdology described in

  • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

and are summarized as follows:

Feature Description
Nuclear Size #pixels of a segmented nucleus
Nuclear Voronoi Size #pixels of the voronoi region, where the segmented nucleus resides
Aspect Ratio Aspect ratio of the segmented nucleus
Major Axis Length of Major axis of the segmented nucleus
Minor Axis Length of Minor axis of the segmented nucleus
Rotation Angle between major axis and x axis of the segmented nucleus
Bending Energy Mean squared curvature values along nuclear contour
STD Curvature Standard deviation of absolute curvature values along nuclear contour
Abs Max Curvature Maximum absolute curvature values along nuclear contour
Mean Nuclear Intensity Mean intensity in nuclear region measured in gray scale
STD Nuclear Intensity Standard deviation of intensity in nuclear region measured in gray scale
Mean Background Intensity Mean intensity of nuclear background measured in gray scale
STD Background Intensity Standard deviation of intensity of nuclear background measured in gray scale
Mean Nuclear Gradient Mean gradient within nuclear region measured in gray scale
STD Nuclear Gradient Standard deviation of gradient within nuclear region measured in gray scale

The construction of cellular morphometric context representation is devired using the methodology described in

  • Hang Chang, Alexander Borowsky, Paul T. Spellman and Bahram Parvin, "Classification of Tumor Histology via Morphometric Context," Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, Portland, OR, 2013, pp. 2203-2210.