Name
Title | ||
|---|---|---|
Information-Theoretic Active Contour Model for Microscopy Image Segmentation Using Texture. |
Abstract | ||
|---|---|---|
High throughput technologies have increased the need for automated image analysis in a wide variety of microscopy techniques. Geometric active contour models provide a solution to automated image segmentation by incorporating statistical information in the detection of object boundaries. A statistical active contour may be defined by taking into account the optimisation of an information-theoretic measure between object and background. We focus on a product-type measure of divergence known as Cauchy-Schwartz distance which has numerical advantages over ratio-type measures. By using accurate shape derivation techniques, we define a new geometric active contour model for image segmentation combining Cauchy-Schwartz distance and Gabor energy texture filters. We demonstrate the versatility of this approach on images from the Brodatz dataset and phase-contrast microscopy images of cells. |
Year | Venue | Field |
|---|---|---|
2016 | CIBB | Active contour model,Computer vision,Scale-space segmentation,Computer science,Image texture,Image segmentation,Region growing,Artificial intelligence,Microscopy,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Veronica Biga | 1 | 0 | 0.68 |
| Daniel Coca | 2 | 106 | 20.12 |