|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM34397701X |
| 003 |
DE-627 |
| 005 |
20231226021708.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TUFFC.2022.3193640
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1146.xml
|
| 035 |
|
|
|a (DE-627)NLM34397701X
|
| 035 |
|
|
|a (NLM)35877808
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Lee, Haeyun
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 30.08.2022
|
| 500 |
|
|
|a Date Revised 06.09.2022
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a 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 article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) 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 DCAIP consists of deep learning networks based on this attention module. The DCAIP 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 (PR) curve (AUPRC). 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
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Lee, Moon Hwan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Youn, Sangyeon
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lee, Kyungsu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lew, Hah Min
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Hwang, Jae Youn
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on ultrasonics, ferroelectrics, and frequency control
|d 1986
|g 69(2022), 9 vom: 25. Sept., Seite 2638-2650
|w (DE-627)NLM098181017
|x 1525-8955
|7 nnns
|
| 773 |
1 |
8 |
|g volume:69
|g year:2022
|g number:9
|g day:25
|g month:09
|g pages:2638-2650
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TUFFC.2022.3193640
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_22
|
| 912 |
|
|
|a GBV_ILN_24
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 69
|j 2022
|e 9
|b 25
|c 09
|h 2638-2650
|