Literature DB >> 26609802

Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging.

Jose Juan Garcia-Hernandez1, Wilfrido Gomez-Flores2, Javier Rubio-Loyola3.   

Abstract

Medical images (MI) are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Due to their importance, this study is focused on breast ultrasound (BUS), which is the main adjunct for mammography to detect common breast lesions among women worldwide. On the other hand, aiming to enhance data security, image fidelity, authenticity, and content verification in e-health environments, MI watermarking has been widely used, whose main goal is to embed patient meta-data into MI so that the resulting image keeps its original quality. In this sense, this paper deals with the comparison of two watermarking approaches, namely spread spectrum based on the discrete cosine transform (SS-DCT) and the high-capacity data-hiding (HCDH) algorithm, so that the watermarked BUS images are guaranteed to be adequate for a computer-aided diagnosis (CADx) system, whose two principal outcomes are lesion segmentation and classification. Experimental results show that HCDH algorithm is highly recommended for watermarking medical images, maintaining the image quality and without introducing distortion into the output of CADx.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Breast ultrasound; Computer-aided diagnosis; Data security; Segmentation; Watermarking

Mesh:

Year:  2015        PMID: 26609802     DOI: 10.1016/j.compbiomed.2015.10.014

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  3 in total

1.  Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology.

Authors:  Tao Li; Liling Long
Journal:  J Med Syst       Date:  2019-12-14       Impact factor: 4.460

2.  Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network.

Authors:  Baoru Han; Jinglong Du; Yuanyuan Jia; Huazheng Zhu
Journal:  J Healthc Eng       Date:  2021-07-01       Impact factor: 2.682

3.  Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

Authors:  Jae-Hong Lee; Do-Hyung Kim; Seong-Nyum Jeong; Seong-Ho Choi
Journal:  J Periodontal Implant Sci       Date:  2018-04-30       Impact factor: 2.614

  3 in total

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