Name
Title | ||
|---|---|---|
White Blood Cell Nuclei Segmentation Using Level Set Methods And Geometric Active Contours |
Abstract | ||
|---|---|---|
A new method for segmenting white blood cells nuclei in microscopic images is presented. Challenges to accurate segmentation include intra-class variation of the nuclei cell boundaries, non-uniform illumination, and changes in the cell topology due to its orientation and stage of maturity. In this research, level set methods and geometric active contours are used to segment the nucleus of white blood cells from the cytoplasm and the cell wall. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are less computationally complex and adapt better to the curve topology of the cell boundary than parametric active contours, which have been previously used for white blood cell segmentation. Segmentation performance is compared with other segmentation methods using the Berkeley benchmark database and the proposed method is shown to be superior using various indices. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA) | geometric active contours, parametric active contours, level set methods, white blood cell segmentation |
Field | DocType | Citations |
Computer vision,Nucleus,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Level set,Image segmentation,Parametric statistics,Artificial intelligence,Nuclei segmentation,White blood cell | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khamael Al-Dulaimi | 1 | 1 | 1.70 |
| Tomeo-Reyes, I. | 2 | 7 | 4.86 |
| Jasmine Banks | 3 | 74 | 10.71 |
| Vinod Chandran | 4 | 514 | 61.49 |