A Segmentation Approach for Mammographic Images and Its Clinical Value

被引：0
|
作者：
Egoshin, I. [1 ]
Pasynkov, D. [2 ]
Kolchev, A. [3 ]
Kliouchkin, I. [4 ]
Pasynkova, O. [1 ]
机构：
[1] Mari State Univ, Yoshkar Ola 424000, Russia
[2] Oncol Dispenser Mari El Republ, Yoshkar Ola 424037, Russia
[3] Kazan Fed Univ, Kazan 420008, Russia
[4] Kazan State Med Univ, Kazan 420012, Russia
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON MICROWAVES, ANTENNAS, COMMUNICATIONS AND ELECTRONIC SYSTEMS (COMCAS) | 2017年
关键词：
mammography; image analysis; breast cancer; computer-aided detection; segmentation; SCREENING MAMMOGRAPHY; BREAST DENSITY; CANCERS; INTERVAL; WOMEN; US;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We developed the nested contour algorithm (NCA) - a segmentation method for X-ray mammography images and tested it on a set of 1532 images of 356 women with morphologically proven breast cancer of various characteristics located on different density background. As a result NCA correctly marked 48 of 52 (92.31 %) star-like lesions, 12 of 14 (85.71 %) architectural distortions, 51 of 58 (87.93 %) lesions with irregular shape and unclear margin, all 18 lobular and round lesions, 17 of 18 (94.4 %) partially visualized lesions, 13 of 18 (72.2 %) asymmetric areas and 7 of 16 (43.8 %) unclearly visible or invisible lesions. Overall sensitivity of NCA in our set was 90.73 % (323 of 356 cases). The mean rate of false-positive marks was 1.3 per image - for ACR A-B mammograms and 1.8 - for ACR C-D mammograms.
引用
收藏
页码：306 / 311
页数：6
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