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Detection of pigment network in dermatoscopy images using texture analysis
| Content Provider | PubMed Central |
|---|---|
| Author | Murali, Anantha Moss, Randy H. Stoecker, William V. |
| Copyright Year | 2004 |
| Abstract | Dermatoscopy, also known as dermoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such prominent feature is the pigment network. Two texture-based algorithms are developed for the detection of pigment network. These methods are applicable to various texture patterns in dermatoscopy images, including patterns that lack fine lines such as cobblestone, follicular, or thickened network patterns. Two texture algorithms, Laws energy masks and the neighborhood gray-level dependence matrix (NGLDM) large number emphasis, were optimized on a set of 155 dermatoscopy images and compared. Results suggest superiority of Laws energy masks for pigment network detection in dermatoscopy images. For both methods, a texel width of 10 pixels or approximately 0.22 mm is found for dermatoscopy images. |
| Related Links | http://dx.doi.org/10.1016/j.compmedimag.2004.04.002 |
| Ending Page | 234 |
| Page Count | 10 |
| Starting Page | 225 |
| File Format | |
| ISSN | 08956111 |
| e-ISSN | 18790771 |
| Journal | Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society |
| Issue Number | 5 |
| Volume Number | 28 |
| Language | English |
| Publisher Date | 2004-07-01 |
| Access Restriction | Open |
| Subject Keyword | Radiological and Ultrasound Technology Health Informatics Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology |
