کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1761419 | 1019647 | 2009 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
آکوستیک و فرا صوت
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance](/preview/png/1761419.png)
چکیده انگلیسی
Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a 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 an 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%, FNÂ =Â 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically. (E-mail: hengda.cheng@usu.edu)
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ultrasound in Medicine & Biology - Volume 35, Issue 8, August 2009, Pages 1309-1324
Journal: Ultrasound in Medicine & Biology - Volume 35, Issue 8, August 2009, Pages 1309-1324
نویسندگان
Bo Liu, H.D. Cheng, Jianhua Huang, Jiawei Tian, Jiafeng Liu, Xianglong Tang,