https://scholars.lib.ntu.edu.tw/handle/123456789/477793
標題: | Whole Breast Lesion Detection Using Naive Bayes Classifier for Portable Ultrasound | 作者: | Yang M.-C. CHIUN-SHENG HUANG Chen J.-H. Chang R.-F. |
公開日期: | 2012 | 卷: | 38 | 期: | 11 | 起(迄)頁: | 1870-1880 | 來源出版物: | Ultrasound in Medicine and Biology | 摘要: | In recent years, portable PC-based ultrasound (US) imaging systems developed by some companies can provide an integrated computer environment for computer-aided diagnosis and detection applications. In this article, an automatic whole breast lesion detection system based on the naive Bayes classifier using the PC-based US system Terason t3000 (Terason Ultrasound, Burlington, MA, USA) with a hand-held probe is proposed. To easily retrieve the US images for any regions of the breast, a clock-based storing system is proposed to record the scanned US images. A computer-aided detection (CAD) system is also developed to save the physicians' time for a huge volume of scanned US images. The pixel classification of the US is based on the naive Bayes classifier for the proposed lesion detection system. The pixels of the US are classified into two types: lesions or normal tissues. The connected component labeling is applied to find the suspected lesions in the image. Consequently, the labeled two-dimensional suspected regions are separated into two clusters and further checked by two-phase lesion selection criteria for the determination of the real lesion, while reducing the false-positive rate. The free-response operative characteristics (FROC) curve is used to evaluate the detection performance of the proposed system. According to the experimental results of 31 cases with 33 lesions, the proposed system yields a 93.4% (31/33) sensitivity at 4.22 false positives (FPs) per hundred slices. Moreover, the speed for the proposed detection scheme achieves 12.3 frames per second (fps) with an Intel Dual-Core Quad 3 GHz processor and can be also effectively and efficiently used for other screening systems. ? 2012 World Federation for Ultrasound in Medicine & Biology. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867000020&doi=10.1016%2fj.ultrasmedbio.2012.07.006&partnerID=40&md5=f2282c1f01d787d571fd441541dd3539 https://scholars.lib.ntu.edu.tw/handle/123456789/477793 |
ISSN: | 0301-5629 | DOI: | 10.1016/j.ultrasmedbio.2012.07.006 | SDG/關鍵字: | Breast lesion detection; Computer-aided detection; Connected component labeling; Detection performance; Detection scheme; Dual-core; False positive; Frames per seconds; Integrated computer environment; Lesion detection; Naive Bayes classifiers; Normal tissue; PC-based; Pixel classification; Portable ultrasound; Screening system; Selection criteria; Ultrasound imaging; Classifiers; Computer aided diagnosis; Learning systems; Mammography; Medical imaging; Pixels; Tissue; Ultrasonics; Ultrasonic applications; adult; aged; article; breast cancer; breast carcinoma; breast fibroadenoma; breast papilloma; breast tumor; cancer diagnosis; carcinoma in situ; classifier; clinical article; colloid carcinoma; computer assisted diagnosis; controlled study; echograph; echography; false positive result; female; fibrocystic breast disease; human; image analysis; infiltrating ductal carcinoma; naive Bayes classifier; priority journal; sensitivity analysis; Adult; Algorithms; Artificial Intelligence; Bayes Theorem; Breast Neoplasms; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Middle Aged; Miniaturization; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Mammary; Young Adult |
顯示於: | 醫學系 |
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