Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Engineering

PDF

Missouri University of Science and Technology

Chemistry Faculty Research & Creative Works

Humans

Publication Year

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Sector Expansion And Elliptical Modeling Of Blue-Gray Ovoids For Basal Cell Carcinoma Discrimination In Dermoscopy Images, Pelin Guvenc, Robert W. Leander, Serkan Kefel, William V. Stoecker, Ryan K. Rader, Kristen A. Hinton, Sherea Monica Stricklin, Harold S. Rabinovitz, Margaret C. Oliviero, Randy Hays Moss Feb 2013

Sector Expansion And Elliptical Modeling Of Blue-Gray Ovoids For Basal Cell Carcinoma Discrimination In Dermoscopy Images, Pelin Guvenc, Robert W. Leander, Serkan Kefel, William V. Stoecker, Ryan K. Rader, Kristen A. Hinton, Sherea Monica Stricklin, Harold S. Rabinovitz, Margaret C. Oliviero, Randy Hays Moss

Chemistry Faculty Research & Creative Works

Background: Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics.

Methods: Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a …


Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi Nov 2009

Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi

Chemistry Faculty Research & Creative Works

Objective: To identify and analyze subtypes of globules based on size, shape, network connectedness, pigmentation, and distribution to determine which globule types and globule distributions are most frequently associated with a diagnosis of malignant melanoma. Design: Retrospective case series of dermoscopy images with globules. Setting: Private dermatology practices. Participants: Patients in dermatology practices. Intervention: Observation only. Main Outcome Measure: Association of globule types with malignant melanoma. Results: The presence of large globules (odds ratio [OR], 5.25) and globules varying in size (4.72) or shape (5.37) had the highest ORs for malignant melanoma among all globule types and combinations studied. Classical …


Melanoma And Seborrheic Keratosis Differentiation Using Texture Features, Srinivas V. Deshabhoina, Scott E. Umbaugh, William V. Stoecker, Randy Hays Moss, Subhashini K. Srinivasan Nov 2003

Melanoma And Seborrheic Keratosis Differentiation Using Texture Features, Srinivas V. Deshabhoina, Scott E. Umbaugh, William V. Stoecker, Randy Hays Moss, Subhashini K. Srinivasan

Chemistry Faculty Research & Creative Works

Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.

Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space. …