Automatic seed selection is an important and crucial step toward the boundary detection in ultrasound B-scan images. This paper focuses on a methodological framework that can automatically detect a seed point of an ultrasound image by using texture features. Based on the selected seeds of cluster the ultrasound images are segmented using active contour, K-means and Otsu methods. The comparative analysis of these segmentation techniques is also reported. The proposed method is applied on 116 ultrasound images in which 45 are benign cases and 71 malignant cases. The quantitative experimental results show that the proposed method can successfully find an accurate seed point based on texture features and it has the ability to segment the image with high accuracy of 89.65 %. The proposed method is faster and performs more accurate segmentation than existing algorithms.