Automatic detection of white areas in dermoscopy images

This thesis presents an algorithm for automatically segmenting the white areas in dermoscopy images. The algorithm includes preprocessing of images, plotting the histogram (RGB) and calculating the average and standard deviation values (RGB) of the lesion. A threshold value for each color plane is determined using these parameters and white areas are automatically segmented. Various image features such as decile percentages and globule features are extracted and given to a neural network. The proposed algorithm has produced a maximum diagnostic accuracy of 94.67% and, when the lesions which touch the image border are removed from the set, the diagnostic accuracy is 96.17% using Receiver Operating Characteristic curve analysis. The code is implemented in MATLAB® 7.4.0.287 (R2007a) --Abstract, page iii

Robotic Nursing Assistants: Human Temperature Measurement Case Study

In this study, we present a human temperature measurement case implementation for hospital patients using a mobile robotic platform (PR2). The main focus of the study is to apply computer vision methods, specifically optical character recognition (OCR) with template matching, in order to read a non-contact thermometer screen while PR2 is measuring patient\u27s temperature. Parameter analysis is done for thermometer digit detection, and parameters with best results are selected for human tests. The tests are designed including human subjects as patients, a tablet for human-robot interaction (HRI), and the PR2 robot. Human subject test are performed with 8 volunteers over 2 days in Assistive Robotics Laboratory at the University of Texas at Arlington Research Institute (UTARI). Results and observations of human subject tests are provided. These activities are a part of a larger effort to establish adaptive robotic nursing assistants (ARNA) for physical tasks in hospital environments

Concentric Decile Segmentation of White and Hypopigmented Areas in Dermoscopy Images of Skin Lesions Allows Discrimination of Malignant Melanoma

Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. White areas, prominent in early malignant melanoma and melanoma in situ, contribute to early detection of these lesions. An adaptive detection method has been investigated to identify white and hypopigmented areas based on lesion histogram statistics. Using the Euclidean distance transform, the lesion is segmented in concentric deciles. Overlays of the white areas on the lesion deciles are determined. Calculated features of automatically detected white areas include lesion decile ratios, normalized number of white areas, absolute and relative size of largest white area, relative size of all white areas, and white area eccentricity, dispersion, and irregularity. Using a back-propagation neural network, the white area statistics yield over 95% diagnostic accuracy of melanomas from benign nevi. White and hypopigmented areas in melanomas tend to be central or paracentral. The four most powerful features on multivariate analysis are lesion decile ratios. Automatic detection of white and hypopigmented areas in melanoma can be accomplished using lesion statistics. A neural network can achieve good discrimination of melanomas from benign nevi using these areas. Lesion decile ratios are useful white area features