## 1 Introduction  
Breast cancer (BC) is one of the main reasons of demises among women worldwide [1]. According to the report, one out of twelve female may be affected with BC. The World Health Organization (WHO) documented that, approximately 508,000 women died due to this disease in 2011 [2]. In spite of this, it is possible to defeat breast cancer if it is diagnosed at the initial stage. The mammography imaging is considered an appropriate method of breast cancer diagnosis. However, BUS is employed as the supportive tool to assess mammographic finding, profound masses, indecisive mammograms, and to guide biopsies [3]. BUS is also the main procedure prescribed to assess breast ailments in females especially below the age of 30 due to low cost and easy access that can accurately differentiate those types as compared to other imaging procedures. However, proper diagnosis by BUS involves trained and experienced radiologists to identify dense and cystic breast lesion. The report of radiologist is based on visual inspection of BUS images only, so it is difficult to diagnose it as malignant or benign [4; 5]. Furthermore, BUS is operator-dependent modality [5] that comprises artifacts (i.e., speckle noise etc. ), which degrades the image quality. CADx systems using machine learning approaches can be developed for diagnosis of breast anomalies and tumors classification. The CADx system assists radiologists in the evaluation of breast abnormalities with reliability and accuracy [6; 7]. Furthermore, CADx may avoid extra efforts during an examination and diagnostic errors of physicians that are made due to fatigue and workload. A CADx framework usually comprises of four important steps: Preprocessing, segmentation, feature extraction, and feature classification [8; 9]. The efficacy of a CADx framework depends on employed features that are mostly acquired using expert knowledge. Several researchers proposed the CADx system to detect breast abnormalities using BUS images. Image segmentation is an essential phase in the CADx framework. Different classical approaches are used for BUS segmentation such as thresholding, region growing, and watershed [10]. Thresholding is the simple and speedy method of segmentation. Threshold value is constant over the whole image in global thresholding whereas, in case of local thresholding, value varies over the local features. Global thresholding cannot perform well in noisy and low contrast images [10]. [PERSON] et al. employed [11] the automatic seed point selection technique for region-growing method. In this method, thresholding was utilized to create a group of selected regions and then tumor region is determined based on local feature, size of region, and location. Watershed is a robust technique and it yields accurate segmentation than region growing and thresholding. However, the main issue faced in watershed is the selection of marker, therefore, [PERSON] et al. [12] employed internal and external markers using the Beucher gradient [13] to prevent over-segmentation. The efficacy of CADx framework mostly depends upon the appropriate feature set. A variety of features are obtained from BUS images and classified through machine learning. The available and most employed features are typically separated into two major groups: Texture-based and shape-based [6]. Numerous texture features are utilized for classing ultrasound breast tumors. For example, [PERSON] et al. [14] performed texture analysis of BUS images that applied gray-scale invariant features via ranklet transform and used support vector machine (SVM) to isolate the malignant lesions from benign ones. [PERSON] et al. [15] employed fractal features, textural features from spatial gray-level dependence (SGLD) matrices, and histogram-based features. The stepwise regression is applied for choosing an optimal subset of features and used fuzzy SVM (FSVM), artificial neural network (ANN) and SVM, and claimed that FSVM produces better results. [PERSON] et al.