Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix
| Autor: | Naima Taifi, Khaddouj Taifi, Muhammad Sarfraz, Said Safi, Mohamed Fakir |
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| Rok vydání: | 2022 |
| Předmět: |
Gray level
0209 industrial biotechnology Co-occurrence matrix 020901 industrial engineering & automation business.industry Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pattern recognition 02 engineering and technology Artificial intelligence business Contourlet |
| DOI: | 10.4018/978-1-6684-7136-4.ch006 |
| Popis: | This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT. |
| Databáze: | OpenAIRE |
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