Breast mass image segmentation and classification based on adaptive energy offset field-CV(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第8期
- Page:
- 1010-1016
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Breast mass image segmentation and classification based on adaptive energy offset field-CV
- Author(s):
- WANG Xiaoyi; XING Suxia; WANG Yu; CAO Yu; SHEN Nan; PAN Ziyan
- School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
- Keywords:
- Keywords: breast mass image segmentation energy offset field CV model support vector machine
- PACS:
- R318；TP301.6
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.08.014
- Abstract:
- Abstract: Objective To propose a method based on adaptive energy offset field-CV (AEOF-CV) for breast mass image segmentation and classification, thereby improving the accuracy and efficiency of breast cancer detection. Methods Firstly, median filtering, threshold segmentation and regional connectivity were used for image preprocessing to remove image noise. Then the image was enhanced by combining gamma transformation and morphological operation. Subsequently, AEOF-CV was used to improve the accuracy of low-contrast image segmentation for realizing breast mass image segmentation and obtaining the regions of interest. Finally, different feature extraction methods were combined with support vector machine for identifying whether there was a mass in the regions of interest and whether the mass was benign or malignant. Results A total of 350 images in DDSM database were tested. The experimental results showed that breast mass image segmentation based on AEOF-CV could obtain a clear external contour of the mass, with good robustness, and the misclassification rate was 0.212 0. The recognition rate for non-mass samples was 94.57%, and the recognition rates for malignant masses and benign masses were 97.91% and 96.96%, respectively. The average recognition rate of the proposed method reached 94.00%. Conclusion Breast mass image segmentation based on AEOF-CV has a good performance, with the misclassification rate reduced by 19.17% as compared with CV method, and the precision and recall rates are up to 0.851 9 and 0.836 5. The proposed method which has a good global analysis performance is an effective method for breast mass image segmentation and can provide a reliable basis for subsequent pattern recognition.
Keywords: breast mass image segmentation energy offset field CV model support vector machine
Last Update: 2020-08-27