상세정보
학술지
Fully Automated Nipple Detection in Digital Breast Tomosynthesis
- 발행일
- 201705
- 출처
- Computer Methods and Programs in Biomedicine, v.143, pp.113-120
- ISSN
- 0169-2607
- 출판사
- Elsevier
- 초록
- Background and objective We propose a nipple detection algorithm for use with digital breast tomosynthesis (DBT) images. DBT images have been developed to overcome the weaknesses of 2D mammograms for denser breasts by providing 3D breast images. The nipple location acts as an invaluable landmark in DBT images for aligning the right and left breasts and describing the relative location of any existing lesions. Methods Nipples may be visible or invisible in a breast image, and therefore a nipple detection method must be able to detect the nipples for both cases. The detection method for visible nipples based on their shape is simple and highly efficient. However, it is difficult to detect invisible nipples because they do not have a prominent shape. Fibroglandular tissue in a breast is anatomically connected with the nipple. Thus, the nipple location can be detected by analyzing the location of such tissue. In this paper, we propose a method for detecting the location of both visible and invisible nipples using fibroglandular tissue and changes in the breast area. Results Our algorithm was applied to 138 DBT images, and its nipple detection accuracy was evaluated based on the mean Euclidean distance. The results indicate that our proposed method achieves a mean Euclidean distance of 3.10혻짹혻2.58혻mm. Conclusions The nipple location can be a very important piece of information in the process of a DBT image registration. This paper presents a method for the automatic nipple detection in a DBT image. The extracted nipple location plays an essential role in classifying any existing lesions and comparing both the right and left breasts. Thus, the proposed method can help with computer-aided detection for a more efficient DBT image analysis.
- KSP 제안 키워드
- Computer-aided Detection(CADe), Detection Method, Detection accuracy, Detection algorithm, Digital breast tomosynthesis(DBT), Image Analysis, Relative Location, euclidean distance, fully automated, highly efficient, image registration