Cited time in webofscience Cited time in scopus

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

Title
Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image
Author(s)
Lee, HaeyunLee, Moon HwanYoun, SangyeonLee, KyungsuLew, Hah MinHwang, Jae Youn
Issued Date
2022-09
Citation
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, v.69, no.9, pp.2638 - 2650
Type
Article
Author Keywords
Deep learningImage PriorImage restorationImage segmentationImagingInformation filtersSelf-supervised LearningSpeckleSpeckle ReductionUltrasonic imagingUltrasound Image
Keywords
ENHANCEMENTSUPERPIXELSTRANSFORMALGORITHM
ISSN
0885-3010
Abstract
The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this paper, we propose a deep content-aware image prior with a content-aware attention module for superior despeckling of ultrasound images without clean images. For the image prior, we developed a content-aware attention module to deal with the content information in an input image. In this module, super-pixel pooling is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The deep content-aware image prior consists of deep learning networks based on this attention module. The deep content-aware image prior is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall curve. The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems. IEEE
URI
http://hdl.handle.net/20.500.11750/17083
DOI
10.1109/TUFFC.2022.3193640
Publisher
Institute of Electrical and Electronics Engineers
Related Researcher
  • 황재윤 Hwang, Jae Youn 전기전자컴퓨터공학과
  • Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Department of Electrical Engineering and Computer Science MBIS(Multimodal Biomedical Imaging and System) Laboratory 1. Journal Articles

qrcode

  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE