Name
Abstract | ||
|---|---|---|
We research a mobile imaging system for early diagnosis of melanoma. Different from previous work, we focus on smartphone-captured images, and propose a detection system that runs entirely on the smartphone. Smartphone-captured images taken under loosely-controlled conditions introduce new challenges for melanoma detection, while processing performed on the smartphone is subject to computation and memory constraints. To address these challenges, we propose to localize the skin lesion by combining fast skin detection and fusion of two fast segmentation results. We propose new features to capture color variation and border irregularity which are useful for smartphone-captured images. We also propose a new feature selection criterion to select a small set of good features used in the final lightweight system. Our evaluation confirms the effectiveness of proposed algorithms and features. In addition, we present our system prototype which computes selected visual features from a user-captured skin lesion image, and analyzes them to estimate the likelihood of malignance, all on an off-the-shelf smartphone. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6945178 | EMBC |
Keywords | Field | DocType |
selected visual feature selection criterion,mobile imaging system,fast skin fusion,image fusion,maximum likelihood estimation,image segmentation,malignance likelihood estimation,smartphone captured image segmentation,cancer,color variation capturing,fast skin detection,user-captured skin lesion image,smart phones,melanoma detection,feature selection,mobile computing,early melanoma diagnosis,skin,medical image processing,image colour analysis,off-the-shelf smartphone | Computer vision,Feature selection,Skin lesion,Computer science,Medical imaging,Segmentation,Artificial intelligence,Melanoma detection,Computation | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thanh-Toan Do | 1 | 173 | 24.22 |
| Yiren Zhou | 2 | 18 | 2.79 |
| Haitian Zheng | 3 | 22 | 4.44 |
| Ngai-Man Cheung | 4 | 750 | 67.36 |
| Dawn Koh | 5 | 0 | 0.68 |