Name
Abstract | ||
|---|---|---|
Reflectance confocal microscopy (RCM) allows in-vivo microscopic examination of human skin and is emerging as a powerful tool for a wide range of dermatological problems. Clinical use of RCM is limited by the need for trained experts to interpret images and the lack of supporting tools for quantitative evaluation of the images, especially in large datasets. The first task in understanding RCM images is to understand and produce a segmentation of the anatomical strata of human skin. This work presents such an algorithm using only weak supervision, in the form of labels for whole en-face sections, to learn a per pixel segmentation of a complete RCM depth stack. Using a bag-of- features representation for image appearance, and a conditional random field model for strata labels through the depth of the skin, a structured support vector machine was trained to label individual pixels. The approach was developed and tested on a dataset of 308 depth stacks from 54 volunteers, consisting of 16,144 total en-face sections. This approach accurately classified 85.7% of sections in the test set, and was able to detect underlying changes in the skin strata thickness with age. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DICTA.2015.7371231 | 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
anatomical skin segmentation,reflectance confocal microscopy,weak labels,in-vivo microscopic examination,human skin,dermatological problems,image interpretation,image quantitative evaluation,anatomical strata segmentation,learning,per pixel segmentation,RCM depth stack,bag-of-features representation,image appearanc,conditional random field model,strata labels,skin depth,structured support vector machine training,pixel labeling,classification,change detection,skin strata thickness | Conditional random field,Structured support vector machine,Computer vision,Histogram,Pattern recognition,Segmentation,Computer science,Image segmentation,Feature extraction,Artificial intelligence,Pixel,Test set | Conference |
Citations | PageRank | References |
3 | 0.53 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel C. Hames | 1 | 3 | 0.87 |
| Marco Ardigo | 2 | 3 | 0.87 |
| H. Peter Soyer | 3 | 58 | 5.71 |
| Andrew P. Bradley | 4 | 2087 | 195.95 |
| Tarl W Prow | 5 | 3 | 1.21 |