Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Using Local Dependencies within Batches to Improve Large Margin Classifiers
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Confocal reflectance microscopy is an emerging modality, for dermatology applications, especially for in-situ and bedside detection of skin cancers. As this technology gains acceptance, automated processing methods become increasingly important to develop. Since the dominant internal feature of the skin is the epidermis/dermis boundary, it has been chosen as the initial target for this development. This boundary is a complex corrugated 3D layer marked by optically subtle changes and features. Indeed, even trained clinicians in an attempt at validation of our early work, were unable to precisely and reliably locate the boundary within optical resolution. Thus here we propose to detect two boundaries, a lower boundary for the epidermis and an upper boundary for the dermis thus trapping the epidermis/dermis boundary. We use a novel combined segmentation/classification approach applied to z-sequences of tiles in the 3D stack. The approach employs a sequential classification on texture features, selected via on-line feature selection, to minimize the labeling required and to cope with high inter- and intra-subject variability and low optical contrast. Initial results indicate the ability of our approach to find these two boundaries successfully for most of the z-sequences from the stack.