Learning the Topological Properties of Brain Tumors
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An effective automated grading system for HCC in biopsy images
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Biomedical image analysis on a cooperative cluster of GPUs and multicores
Proceedings of the 22nd annual international conference on Supercomputing
Colorectal Polyps Detection Using Texture Features and Support Vector Machine
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Journal of Signal Processing Systems
Stroma classification for neuroblastoma on graphics processors
International Journal of Data Mining and Bioinformatics
ECM-aware cell-graph mining for bone tissue modeling and classification
Data Mining and Knowledge Discovery
Computer-aided classification of zoom-endoscopical images using Fourier filters
IEEE Transactions on Information Technology in Biomedicine
A boosting cascade for automated detection of prostate cancer from digitized histology
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features
Journal of Medical Systems
Computers in Biology and Medicine
Cell-graph coloring for cancerous tissue modelling and classification
Multimedia Tools and Applications
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
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The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P