KEYWORDS: Digital breast tomosynthesis, Signal to noise ratio, Computer aided diagnosis and therapy, Breast, Mammography, Detection and tracking algorithms, Reconstruction algorithms, Medical research, 3D image reconstruction
A computer-aided detection (CADe) system for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) volumes was suggested. The system consisted of prescreening, MC detecting, clustering, and falsepositive reduction steps. In the prescreening stage, the MC-like objects were enhanced by a multiscale-based 3D calcification response function. A connected component segmentation method was used to detect cluster seed objects, which were considered as potential clustering centers of MCs. Starting with each cluster seed object as the initial cluster center, a cluster candidate was formed by including nearby MC candidates within a 3D neighborhood of the cluster seed object satisfying the clustering criteria during the clustering step. The size and number of the clustered MCs in a cluster seed candidate were used to reduce the number of FPs. A bounding cube for each MCC was generated for each accepted seed candidates. Then, the overlapping cubes were combined and examined according to the FP reduction criteria. After FP reduction step, we obtained the average number of FPs of 2.47 per DBT volume with sensitivity of 83.3%. Our study indicates the simplified false-positive reduction approach applied to the detection of clustered MCs in DBT is promising as an efficient CADe system.
KEYWORDS: Nipple, Breast, Digital breast tomosynthesis, Mammography, 3D image processing, Mammary gland, Breast cancer, Image processing, Medical research, 3D image reconstruction
Digital Breast Tomosynthesis(DBT) with 3D breast image can improve detection sensitivity of breast cancer more than 2D mammogram on dense breast. The nipple location information is needed to analyze DBT. The nipple location is invaluable information in registration and as a reference point for classifying mass or micro-calcification clusters. Since there are visible nipple and invisible nipple in 2D mammogram or DBT, the nipple detection of breast must be possible to detect visible and invisible nipple of breast. The detection method of visible nipple using shape information of nipple is simple and highly efficient. However, it is difficult to detect invisible nipple because it doesn’t have prominent shape. Mammary glands in breast connect nipple, anatomically. The nipple location is detected through analyzing location of mammary glands in breast. In this paper, therefore, we propose a method to detect the nipple on a breast, which has a visible or invisible nipple using changes of breast area and mammary glands, respectively. The result shows that our proposed method has average error of 2.54±1.47mm.
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