Abnormal mass classification in breast mammography using rotation invariant LBP
| Autor: | Haider Adnan Khan, Abdullah Al Helal, Raqibul Mostafa, Khawza I. Ahmed |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.diagnostic_test
Local binary patterns business.industry Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology medicine.disease 030218 nuclear medicine & medical imaging Support vector machine 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Breast cancer Computer-aided diagnosis Histogram 0202 electrical engineering electronic engineering information engineering medicine Mammography 020201 artificial intelligence & image processing Computer vision Artificial intelligence Invariant (mathematics) business |
| Zdroj: | 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). |
| DOI: | 10.1109/ceeict.2016.7873083 |
| Popis: | We present a novel approach for abnormal breast mass classification from digitized mammography images. The proposed framework exploits rotation invariant uniform Local Binary Pattern (LBP) as texture feature. These features are classified using Support Vector Machine (SVM). In addition, we take advantage of the breast mammograms taken from multiple views or angles. We classify breast scans from ‘cranial-caudal’ view and ‘mediolateral-oblique’ view separately, and combine these classification scores to make an improved diagnosis. This reduces the classification error, and achieves higher recognition rate than that of either views individually. The proposed computer aided diagnosis system was evaluated on DDSM (Digital Database for Screening Mammography) data set, and was able to achieve a classification accuracy of 74%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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