W
William V. Stoecker
Researcher at University of Missouri
Publications - 150
Citations - 6449
William V. Stoecker is an academic researcher from University of Missouri. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 40, co-authored 141 publications receiving 5736 citations. Previous affiliations of William V. Stoecker include Emerson Electric & Missouri University of Science and Technology.
Papers
More filters
Journal ArticleDOI
A Methodological Approach to the Classification of Dermoscopy Images
M. Emre Celebi,Hassan A. Kingravi,Bakhtiyar Uddin,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss +6 more
TL;DR: A methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented and the issue of class imbalance is addressed using various sampling strategies and the classifier generalization error is estimated using Monte Carlo cross validation.
Journal ArticleDOI
Lesion border detection in dermoscopy images.
TL;DR: A systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects is presented.
Journal ArticleDOI
Border detection in dermoscopy images using statistical region merging.
M. Emre Celebi,Hassan A. Kingravi,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss,Joseph M. Malters,James M. Grichnik,Ashfaq A. Marghoob,Harold S. Rabinovitz,Scott W. Menzies +10 more
TL;DR: This work has shown that automated border detection is one of the most important steps in the computer‐aided diagnosis of melanoma, because the accuracy of the subsequent steps crucially depends on it.
Journal ArticleDOI
Neural network diagnosis of malignant melanoma from color images
TL;DR: A novel neural network approach is presented for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics and is able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images.
Journal ArticleDOI
Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.
TL;DR: Dermoscopy images of pigmented lesions are most commonly taken at × 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate to obtain accurate skin lesion segmentation from the background skin.