An analysis on breast tissue characterization in combined transform domain using nearest neighbor classifiers
Autor: | V. Sadasivam, B.N. Prathibha |
---|---|
Rok vydání: | 2011 |
Předmět: |
Discrete wavelet transform
medicine.diagnostic_test Contextual image classification Artificial neural network business.industry Computer science Feature extraction Pattern recognition medicine.disease k-nearest neighbors algorithm Breast cancer medicine Discrete cosine transform Mammography Artificial intelligence business |
Zdroj: | 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). |
DOI: | 10.1109/icccet.2011.5762437 |
Popis: | Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severeness of abnormality (benign or malign) using combined transform domain features. The discrete wavelet transform (DWT) features are merged with discrete cosine transform (DCT) features. The method is tested on 212 mammogram images from the MIAS database. The cascaded transform domain proves to be a promising tool for robust classification. It yields 94.39% of accuracy in classification of normal and benign samples, 90.65% of accuracy in classification of normal and malign samples and 78.50 % of accuracy in classification of benign and malign samples. Classification is done with a combination of nearest neighbor (NN) classifiers; kNN, class based NN and density based NN. |
Databáze: | OpenAIRE |
Externí odkaz: |