The effect of filtering algorithms for breast ultrasound lesions segmentation

Abstract

Abstract Breast ultrasound images have a complicated structure, which is difficult to be segmented due to the fact that it has low signal and affected by noise ratio. Recent research concentrated on the Region of Interest (ROI) labeling and ROI segmentation. In order to reduce chances of human error, stages of processing in breast ultrasound images might be different from one another. This research proposes a new image filtering method for breast ultrasound, namely Altered Phase Preserving Dynamic Range Compression (APPDRC). In addition, this paper compares the performance of filtering algorithms, in combination with standard thresholding segmentation. Focusing on the filtering stage, a comparison between the proposed method APPDRC Filter and previous approaches is validated on a dataset of 306 images, namely Inverted Median filter, Multifractal Filter, Hybrid Filter, SRAD filter, and PPDRC. Further, a summary of the work to date on the effect of filtering on lesion segmentation in ultrasound breast images is reported. Jaccard Similarity Index (JSI) is used for evaluation, in which the automated segmentation result is compared with the experienced radiologist's manual delineation. Our results revealed that making the choice of filtering algorithm affects the final segmentation results. Considering Mean JSI, Dice and MCC metrics, the proposed APPDRC Filter achieved the best performance, and outperformed the five evaluated filtering methods.