The nature of statistical learning theory
The nature of statistical learning theory
Segmentation of 3D microscopy data with an energy based interaction model
MEMEA '09 Proceedings of the 2009 IEEE International Workshop on Medical Measurements and Applications
Three-Dimensional Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images
IEEE Transactions on Information Technology in Biomedicine
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The field of high-throughput applications in biomedicine is an always enlarging field. This kind of applications, providing a huge amount of data, requires necessarily semi-automated or fully automated analysis systems. Such systems are typically represented by classifiers capable of discerning from the different types of data obtained (i.e. classes). In this work we present a methodology to improve classification accuracy in the field of 3D confocal microscopy. A set of 3D cellular images (z-stacks) were taken, each depicting HeLa cells with different mutations of the UCE protein ([Mannose-6-Phosphate] UnCovering Enzyme). This dataset was classified to obtain the mutation class from the z-stacks. 3D and 2D features were extracted, and classifications were carried out with cell by cell and z-stack by z-stack approaches, with 2D or 3D features. Also, a classification approach that combines 2D and 3D features is proposed, which showed interesting improvements in the classification accuracy.