Please use this identifier to cite or link to this item:
|Type:||Artigo de periódico|
|Title:||Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis|
|Abstract:||This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.|
medical image analysis
white blood cell image segmentation
|Editor:||Ieee-inst Electrical Electronics Engineers Inc|
|Citation:||Ieee Journal Of Biomedical And Health Informatics. Ieee-inst Electrical Electronics Engineers Inc, v. 17, n. 1, n. 250, n. 256, 2013.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.