Cancer remains one of the major concerns of deaths worldwide. Early
detection is the key point in reducing the cancer mortality. Automatic systems are
needed to assist radiologists in the cancer detection and diagnosis. Hence, there are
strong needs for the development of computer aided diagnosis (CAD) systems
which have the capability to help radiologists in decision making. The aim of this
work is to develop a computer aided system for breast cancer diagnosis in ultrasound
images. The developed system consists of segmentation, feature extraction,
feature selection and classification. The marker controlled watershed technique is
used to segment the region of interest (ROI). In the feature extraction step, the
wavelet transform is applied then the texture and statistical features of ROI are extracted.
In addition, a set of morphological features are extracted directly from ROI in spatial domain.
The obtained features are combined together to produce the feature vector. In order to select
the most discriminative feature, a feature ranking technique is used to determine the capability of
each feature. In the classification step, support vector machine (SVM), classification and regression
trees (CART) and classification rule classifiers are used to classify the ROI as benign or malignant.
The proposed method is validated using 10 fold cross-validation. The results show that classification
rule classifier outperforms SVM and CART classifiers.
Keywords: Breast cancer diagnosis, computer aided system, features extraction, ultrasound breast images.
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