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Segmentation and classification of biological cell images by a multifractal approach

✍ Scribed by N. Lassouaoui; L. Hamami; A. Zerguerras

Publisher
John Wiley and Sons
Year
2003
Tongue
English
Weight
391 KB
Volume
18
Category
Article
ISSN
0884-8173

No coin nor oath required. For personal study only.

✦ Synopsis

Recently, the clinical role of image processing has been developed considerably. The resources of this new technology were exploited for the needs of doctors in their practice. In this study, we propose a computer vision for tracking the uterine collar cancer. Here, we present three stages: preprocessing, segmentation, and classification. The segmentation stage uses a multifractal algorithm based on the computation of the singularity exponents; its role is separating each cell on its core and its cytoplasm, which permits the analysis of each one in the recognition stage for deducing a response about the malignity of the cell. However, the classification is performed by an algorithm of area growth. Knowing that there are four layers in the epithelium, the classification allows for learning the type of each cell in an image for organizing the research in the recognition stage. Thus, we contribute to the creation of a database for the recognition stage. This base contains the core and cytoplasm images with information about the type of each cell. Promising results were obtained with a short execution time that permits the start of the recognition stage.

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