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Full-Text Articles in Engineering
Identification Of Variegated Coloring In Skin Tumors: Neural Network Vs. Rule-Based Induction Methods, Ajaya Durg, William V. Stoecker, J. P. Cookson, Scott E. Umbaugh, Randy Hays Moss
Identification Of Variegated Coloring In Skin Tumors: Neural Network Vs. Rule-Based Induction Methods, Ajaya Durg, William V. Stoecker, J. P. Cookson, Scott E. Umbaugh, Randy Hays Moss
Chemistry Faculty Research & Creative Works
The use of neural networks for automatic identification of variegated coloring, which is believed to be one of the most predictive features for malignant melanoma, is described. The Nestor development system (NDS) was chosen for neural network implementation. At the heart of NDS is a three-layer neural network called a restricted Coulomb energy (RCE) network. The learning scheme and the database for detection of variegated coloring are discussed. Results are reported
Automatic Color Segmentation Algorithms-With Application To Skin Tumor Feature Identification, Scott E. Umbaugh, Randy Hays Moss, William V. Stoecker, G. A. Hance
Automatic Color Segmentation Algorithms-With Application To Skin Tumor Feature Identification, Scott E. Umbaugh, Randy Hays Moss, William V. Stoecker, G. A. Hance
Electrical and Computer Engineering Faculty Research & Creative Works
Two color-image segmentation methods are described. The first is based on a spherical coordinate transform of original RGB data. The second is based on a mathematically optimal transform, the principal components transform (also known as eigenvector, discrete Karhunen-Loeve, or Hotelling transform). These algorithms are applied to the extraction from skin tumor images of various features such as tumor border, crust, hair scale, shiny areas, and ulcer. The results of this research will be used in the development of a computer vision system that will serve as the visual front-end of a medical expert system to automate visual feature identification for …