Open AccessSegmentation of MR Breast Cancer Images based on DWT and K-means algorithm
Author(s) -
Gaoteng Yuan,
Yihui Liu,
Wei Huang
Publication year - 2019
Publication title -
journal of physics conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012025
Subject(s) - segmentation , lumpectomy , computer science , artificial intelligence , breast cancer , feature (linguistics) , radiation therapy , computer vision , pattern recognition (psychology) , algorithm , cancer , medicine , radiology , mastectomy , linguistics , philosophy
Breast-conserving surgery followed by radiotherapy to the whole breast and boost irradiation to the lumpectomy cavity (LC) is the standard strategy for the early stage breast cancer patients. Accurate segmentation of the target volume is a prerequisite for accurate radiotherapy, which directly affects the success or failure of tumor treatment. The current delineation of target is mainly done by manual drawing, which is time-consuming, laborious and easy to be affected by subjective factors. To solve this problem, we enhance the MR breast images using DWT (discrete wavelet transform) to get more detail of MR image feature firstly. Secondly, we use K-means algorithm to classify the feature vectors and establish the image segmentation model. Finally, compared with the traditional threshold segmentation method, the model is most suitable for automatic delineation of radiotherapy target area and the setting of optimal parameters are obtained. This method can realize the accurate automatic delineation of target area basically, and solve the problem of lack of accuracy and standardization in current tumor bed delineation.