Classification of microcalcifications in digitised mammograms
| dc.contributor.author | Kramer, Dani | |
| dc.date.accessioned | 2014-04-30T07:59:21Z | |
| dc.date.available | 2014-04-30T07:59:21Z | |
| dc.date.issued | 2014-04-30 | |
| dc.description.abstract | In this investigation ^number of image texture analysis techniques for the classification of microcalcifications in digitised mammograms are presented. Microcalcifications are often an early indication of breast cancer, and computer-aided diagnostic techniques are capable of improving diagnostic accuracy. Three categories of image texture features are extracted from regions of interest surrounding clusters of microcalcifications. These comprise a set of statistical texture features based on the co-occurrence matrix, a set of wavelet-based texture signatures and a propose^ third set of texture features. This set, referred to as multiscale statistical texture features, is based on a combination of the other two approaches to texture analysis. The multiscale statistical texture features outperform the other types of texture features in tests using two separate datasets and a k-nn classifier for classification. Improved classification accuracy is also achieved using an artificial neural network for classification. | en_ZA |
| dc.identifier.uri | http://hdl.handle.net10539/14626 | |
| dc.language.iso | en | en_ZA |
| dc.title | Classification of microcalcifications in digitised mammograms | en_ZA |
| dc.type | Thesis | en_ZA |