Handbook of Image and Video Processing
Handbook of Image and Video Processing
A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics
IEEE Transactions on Information Technology in Biomedicine
TMABoost: an integrated system for comprehensive management of tissue microarray data
IEEE Transactions on Information Technology in Biomedicine
Automatic handling of tissue microarray cores in high-dimensional microscopy images
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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Tissue Microarray (TMA) methodology has been recently developed to enable ''genome-scale'' molecular pathology studies. To enable high-throughput screening of TMAs automation is mandatory, both to speed up the process and to improve data quality. In particular, in acquiring digital images of single tissues (core sections) a crucial step is the correct recognition of each tissue position in the array. In fact, further reliable data analysis is based on the exact assignment of each tissue to the corresponding tumor. As most of the times tissue alignment in the microarray grid is far from being perfect, simple strategies to perform proper acquisition do not fit well. The present paper describes a new solution to automatically perform grid location assignment. We developed an ad hoc image processing procedure and a robust algorithm for object recognition. Algorithm accuracy tests and assessment of working constraints are discussed. Our approach speeds up TMA data collection and enables large scale investigation.