The boost graph library: user guide and reference manual
The boost graph library: user guide and reference manual
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Support vector machines for classification of histopathological images of brain tumour astrocytomas
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Bioinformatics
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Four-Color Theorem and Level Set Methods for Watershed Segmentation
International Journal of Computer Vision
IEEE Transactions on Information Technology in Biomedicine
Fractal analysis in the detection of colonic cancer images
IEEE Transactions on Information Technology in Biomedicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Guest Editorial: Special Issue on impacting patient care by mining medical data
Data Mining and Knowledge Discovery
Cell-graph modeling of salivary gland morphology
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Optimal live cell tracking for cell cycle study using time-lapse fluorescent microscopy images
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Comparison of regression tree data mining methods for prediction of mortality in head injury
Expert Systems with Applications: An International Journal
Effective graph classification based on topological and label attributes
Statistical Analysis and Data Mining
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Pathological examination of a biopsy is the most reliable and widely used technique to diagnose bone cancer. However, it suffers from both inter- and intra- observer subjectivity. Techniques for automated tissue modeling and classification can reduce this subjectivity and increases the accuracy of bone cancer diagnosis. This paper presents a graph theoretical method, called extracellular matrix (ECM)-aware cell-graph mining, that combines the ECM formation with the distribution of cells in hematoxylin and eosin stained histopathological images of bone tissues samples. This method can identify different types of cells that coexist in the same tissue as a result of its functional state. Thus, it models the structure-function relationships more precisely and classifies bone tissue samples accurately for cancer diagnosis. The tissue images are segmented, using the eigenvalues of the Hessian matrix, to compute spatial coordinates of cell nuclei as the nodes of corresponding cell-graph. Upon segmentation a color code is assigned to each node based on the composition of its surrounding ECM. An edge is hypothesized (and established) between a pair of nodes if the corresponding cell membranes are in physical contact and if they share the same color. Hence, multiple colored-cell-graphs coexist in a tissue each modeling a different cell-type organization. Both topological and spectral features of ECM-aware cell-graphs are computed to quantify the structural properties of tissue samples and classify their different functional states as healthy, fractured, or cancerous using support vector machines. Classification accuracy comparison to related work shows that the ECM-aware cell-graph approach yields 90.0% whereas Delaunay triangulation and the simple cell-graph approach achieves 75.0 and 81.1% accuracy, respectively.