Heng-Da Cheng

TL;DR: Breast tumor segmentation is an important step of breast ultrasound computer aided diagnosis systems as discussed by the authors, however, it is not a simple task and it requires a large number of procedures.

TL;DR: A fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints achieves state-of-the-art performance compared with that of existing methods.

TL;DR: This paper has proved that evaluating denoising filters is different from image quality assessment, and proposes a novel objective and effective assessment criterion, homogeneity mean difference (HMD), to evaluate the performance of the filters since it can describe the textual and structural information and/or the changes in textual andStructural information well.

TL;DR: The proposed segmentation algorithm is efficient in both selecting seed point and segmenting region of interests (ROIs) in breast ultrasound images by using region growing technique.

TL;DR: The experimental results demonstrate that the proposed computer-aided diagnosis system based on B-Mode ultrasound and color Doppler flow imaging is useful for reducing the unnecessary biopsy and death rate.