Abstract:
Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and u...Show MoreMetadata
Abstract:
Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6%, which was higher than that of Random Walk (76.9%) and Graph-Cut (79.8%).
Published in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
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PubMed ID: 29059956