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2007
IEEE

Multi-Resolution Image Segmentation Using the 2-Point Correlation Functions

9 years 4 months ago
Multi-Resolution Image Segmentation Using the 2-Point Correlation Functions
Recently, the 2-point correlation functions (2-pcfs) were employed in building feature vectors for histological image segmentation. The 2-pcfs serve as estimators of material distributions with respect to the component packing in a multi-phase sample. The multi-phase properties estimated by the 2-pcfs were represented in a tensor structure and a HOSVD-based classification algorithm was developed. In this paper, we employ a multi-resolution framework in the image and the 2-pcfs feature scale-space, in order to achieve significant savings in computational costs. We also propose a new formulation of the HOSVD classifier that learns the relative skew in the feature space. The classifier helps in improving the segmentation accuracy. Our improved results are validated against ground-truth generated from large histology images of mouse placenta.
Firdaus Janoos, M. Okan Irfanoglu, Kishore Mosalig
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ISBI
Authors Firdaus Janoos, M. Okan Irfanoglu, Kishore Mosaliganti, Raghu Machiraju, Kun Huang, Pamela Wenzel, Alain de Bruin, Gustavo Leone
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