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Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
National Institute of Technology (NIT), Tiruchirappalli, India. (Department of Electronics and Communication Engineering)
Regional Cancer Centre, Thiruvanathapuram, India.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2013 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 93, no 10, 2828-2837 p.Article in journal (Refereed) Published
Abstract [en]

Breast cancer is the most frequent cause of cancer induced death among women in the world. Diagnosis of this cancer can be done through radiological, surgical, and pathological assessments of breast tissue samples. A common test for detection of this cancer involves visual microscopic inspection of Fine Needle Aspiration Cytology (FNAC) samples of breast tissue. The result of analysis on this sample by a cytopathologist is crucial for the breast cancer patient. For the assessment of malignancy, the chromatin texture patterns of the cell nuclei are essential. Wavelet transforms have been shown to be good tools for extracting information about texture. In this paper, it has been investigated whether complex wavelets can provide better performance than the more common real valued wavelet transform. The features extracted through the wavelets are used as input to a k-nn classifier. The correct classification results are obtained as 93.9% for the complex wavelets and 70.3% for the real wavelets.

Place, publisher, year, edition, pages
2013. Vol. 93, no 10, 2828-2837 p.
National Category
Medical Image Processing
Research subject
Cell Research; Computerized Image Analysis; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-185094DOI: 10.1016/j.sigpro.2012.06.029ISI: 000321599400005OAI: oai:DiVA.org:uu-185094DiVA: diva2:570736
Available from: 2012-07-13 Created: 2012-11-20 Last updated: 2017-12-07Bibliographically approved

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Issac Niwas, SwamidossBengtsson, Ewert

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Computerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLabDivision of Visual Information and Interaction
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