AUTOMATED SEGMENTATION OF ULTRASONIC BREAST LESIONS USING STATISTICAL TEXTURE CLASSIFICATION AND ACTIVE CONTOUR BASED ON PROBABILITY DISTANCE

作者：Liu Bo; Cheng H D^*; Huang Jianhua; Tian Jiawei; Liu Jiafeng; Tang Xianglong

来源：Ultrasound in Medicine and Biology, 2009, 35(8): 1309-1324.

DOI：10.1016/j.ultrasmedbio.2008.12.007

• Image segmentation
• Texture classification
• Active contour
• Probability distance
• Level set
• Breast ultrasound (BUS) imaging

Because of its complicated structure, low, signal/noise ratio, low contrast and Worry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, It novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by ail experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically. (E-mail: hengda.cheng@usu.

• 出版日期2009-8
• 单位哈尔滨医科大学; 哈尔滨工业大学