Color segmentation of biological microscopic images
| Autor: | Pascal Lescure, Henri Dupoisot, Georges Stamon, Vannary Meas-Yedid |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
Color histogram
business.industry Segmentation-based object categorization Scale-space segmentation Pattern recognition Image segmentation Color space Color quantization Region growing Computer Science::Computer Vision and Pattern Recognition RGB color model Computer vision Artificial intelligence business Mathematics |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.341119 |
| Popis: | The project consists in extracting biological objects from the background of an image in order to determine their three dimensions, namely their thickness. The small size of the photographed objects induces the formation of light interferences. The observed interference colors are related to the properties of the thin objects. Segmentation techniques used for this application are divided into three major types: edge extraction, region growing and splitting, clustering. Generally, edge segmentation works on each separated RGB channel but it leads to a data fusion problem Region growing and splitting methods commonly deal with features extraction. Color is a possible feature. The color image segmentation can be either monodimensional or multidimensional, using classification methods. For the monodimensional segmentation, the gray level is used alone. For the multidimensional case, one can take into account the vectorial character of colors, using color clustering. In this general context the aim of the project is to evaluate how a specific color space can improve the segmentation. Standard color segmentation algorithms are used: (1) C- means; (2) Back-propagation neural network; (3) Learning Vector Quantization. The results are compared with gray level algorithms such as the Otsu thresholding and ISODATA. Applied to each color channel. They show first that there is not only one good color representation space, and secondly, that data clusters are relatively close to each other, which explains why segmentation is so difficult in this class of pictures. |
| Databáze: | OpenAIRE |
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