Search

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 benign competitive set. A total of 22 B- GO features were analyzed for all structures: 21 color features and one size feature. Regarding segmentation, this study utilized a novel sector-based, non-recursive segmentation method to expand the masks applied to the B- GOs and mimicking structures. Results Logistic regression analysis determined that blue chromaticity was the best feature for discriminating true B- GOs in BCC from benign, mimicking structures. Discrimination of malignant structures was optimal when the final B- GO border was approximated by a best-fit ellipse. Using this optimal configuration, logistic regression analysis discriminated the expanded and fitted malignant structures from similar benign structures with a classification rate as high as 96.5%. Conclusions Experimental results show that color features allow accurate expansion and localization of structures from seed areas. Modeling these structures as ellipses allows high discrimination of B- GOs in BCCs from similar structures in benign images. [ABSTRACT FROM AUTHOR]

Background Basal cell carcinoma ( BCC) is the most commonly diagnosed cancer in the USA. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including evolving artificial neural networks ( EANNs) and evolving artificial neural network ensembles. Results Experiment results based on 10-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. [ABSTRACT FROM AUTHOR]