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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Automated breast cancer diagnosis based on GVF-snake segmentation, wavelet features extraction and fuzzy classification
Journal of Signal Processing Systems, Volume 55, No. 1-3, Year 2009
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Description
The automatic diagnosis of breast cancer (BC) is an important, real-world medical problem. This paper proposes a design of automated detection, segmentation, and classification of breast cancer nuclei using a fuzzy logic. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the cytological image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign one with the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a fuzzy C-means (FCM) clustering algorithm to classify the images into malign and benign ones. The implementation of such algorithm has been done using a methodology based on very high speed integrated circuit, hardware description language (VHDL). The design of the circuit is performed by using a CMOS 0.35 μm technology. © 2008 Springer Science+Business Media, LLC.
Authors & Co-Authors
Jihene, Malek E.L.
Tunisia, Monastir
Faculté Des Sciences de Monastir
Sebri, Abderrahim
Tunisia, Monastir
Faculté Des Sciences de Monastir
Mabrouk, S.
Tunisia, Monastir
Faculté Des Sciences de Monastir
Torki, Kholdoun
France, Grenoble
Techniques de L'informatique et de la Microélectronique Pour L'architecture Des Systèmes Intégrés
Tourki, Rached
Tunisia, Monastir
Faculté Des Sciences de Monastir
Statistics
Citations: 56
Authors: 5
Affiliations: 2
Identifiers
Doi:
10.1007/s11265-008-0198-2
ISSN:
19398018
e-ISSN:
19398115
Research Areas
Cancer