Access the full text.
Sign up today, get DeepDyve free for 14 days.
N. Otsu (1979)
A Threshold Selection Method from Gray-Level HistogramsIEEE Trans. Syst. Man Cybern., 9
John Smith, Shih-Fu Chang (1997)
Image and video search engine for the World Wide Web, 3022
M. Hu (1962)
Visual pattern recognition by moment invariantsIRE Trans. Inf. Theory, 8
S. Napel, C. Beaulieu, Cesar Rodriguez, J. Cui, Jiajing Xu, Ankit Gupta, Daniel Korenblum, H. Greenspan, Yongjun Ma, D. Rubin (2010)
Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.Radiology, 256 1
H. Sung, J. Ferlay, R. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray (2021)
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 CountriesCA: A Cancer Journal for Clinicians, 71
Jun Xu, L. Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, A. Madabhushi (2016)
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology ImagesIEEE Transactions on Medical Imaging, 35
T. Pun (1980)
A new method for grey-level picture thresholding using the entropy of the histogramSignal Processing, 2
D. Patel, Darshan Patel (2016)
Improvement in Performance of Image Retrieval using Various Features in CBIR SystemInternational Journal of Computer Applications, 138
Zhiqiong Wang, Junchang Xin, Yukun Huang, Chen Li, Ling Xu, Yang Li, H. Zhang, Huizi Gu, W. Qian (2018)
A similarity measure method combining location feature for mammogram retrieval.Journal of X-ray science and technology, 26 4
J. Oliveira, A. Machado, Guillermo Chávez, A. Lopes, T. Deserno, A. Araújo (2010)
MammoSys: A content-based image retrieval system using breast density patternsComputer methods and programs in biomedicine, 99 3
Geoffrey Hinton (2002)
Training Products of Experts by Minimizing Contrastive DivergenceNeural Computation, 14
Yu Wang, Qian Chen, B. Zhang (1999)
Image enhancement based on equal area dualistic sub-image histogram equalization methodIEEE Trans. Consumer Electron., 45
C. Maggio (2004)
State of the art of current modalities for the diagnosis of breast lesionsEuropean Journal of Nuclear Medicine and Molecular Imaging, 31
(2009)
This PDF file includes: Materials and Methods
C. Shyu, C. Brodley, A. Kak, A. Kosaka, A. Aisen, L. Broderick (1999)
ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image DatabasesComput. Vis. Image Underst., 75
R. Fried, T. Bernholt, U. Gather (2006)
Repeated median and hybrid filtersComput. Stat. Data Anal., 50
H. Tamura, Shunji Mori, Takashi Yamawaki (1978)
Textural Features Corresponding to Visual PerceptionIEEE Transactions on Systems, Man, and Cybernetics, 8
R. Haralick, K. Shanmugam, I. Dinstein (1973)
Textural Features for Image ClassificationIEEE Trans. Syst. Man Cybern., 3
Matthew Zeiler, R. Fergus (2013)
Visualizing and Understanding Convolutional NetworksArXiv, abs/1311.2901
A. Myronenko, Xubo Song (2009)
Point Set Registration: Coherent Point DriftIEEE Transactions on Pattern Analysis and Machine Intelligence, 32
Vijay Raghavan, Gwang Jung, P. Bollmann-Sdorra (1989)
A critical investigation of recall and precision as measures of retrieval system performanceACM Trans. Inf. Syst., 7
Chia-Hung Wei, Sherry Chen, Xiaohui Liu (2012)
Mammogram retrieval on similar mass lesionsComputer methods and programs in biomedicine, 106 3
M. Silverstein, M. Lagios, A. Recht, D. Allred, S. Harms, R. Holland, D. Holmes, L. Hughes, R. Jackman, T. Julian, H. Kuerer, Helen Mabry, D. McCready, K. McMasters, D. Page, S. Parker, H. Pass, M. Pegram, E. Rubin, A. Stavros, D. Tripathy, F. Vicini, Pat Whitworth (2005)
Image-detected breast cancer: state of the art diagnosis and treatment.Journal of the American College of Surgeons, 201 4
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell (2013)
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jingjing Liu, Shaoting Zhang, Wei Liu, Cheng Deng, Yuanjie Zheng, Dimitris Metaxas (2017)
Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing With Iterative QuantizationIEEE Transactions on Circuits and Systems for Video Technology, 27
D. Chandy, Stanly Jeyaraj, S. Selvan (2014)
Texture feature extraction using gray level statistical matrix for content-based mammogram retrievalMultimedia Tools and Applications, 72
BACKGROUND:Breast cancer is one of the most important malignant tumors among women causing a serious impact on women’s lives and mammography is one the most important methods for breast examination. When diagnosing the breast disease, radiologists sometimes may consult some previous diagnosis cases as a reference. But there are many previous cases and it is important to find which cases are the similar cases, which is a big project costing lots of time. Medical image retrieval can provide objective reference information for doctors to diagnose disease. The method of fusing deep features can improve the retrieval accuracy, which solves the “semantic gap” problem caused by only using content features and location features.METHODS:A similarity measure method combining deep feature for mammogram retrieval is proposed in this paper. First, the images are pre-processed to extract the low-level features, including content features and location features. Before extracting location features, registration with the standard image is performed. Then, the Convolutional Neural Network, the Stacked Auto-encoder Network, and the Deep Belief Network are built to extract the deep features, which are regarded as high-level features. Next, content similarity and deep similarity are calculated separately using the Euclidean distance between the query image and the dataset images. The location similarity is obtained by calculating the ratio of intersection to union of the mass regions. Finally, content similarity, location similarity, and deep similarity are fused to form the image fusion similarity. According to the similarity, the specified number of the most similar images can be returned.RESULTS:In the experiment, 740 MLO mammograms are used, which are from women in Northeast China. The content similarity, location similarity, and deep similarity are fused by different weight coefficients. When only considering low-level features, the results are better with fusing 60% content feature similarity and 40% lesion location feature similarity. On this basis, CNN deep similarity, DBN deep similarity, and SAE deep similarity are fused separately. The experiments show that when fusing 60% DBN deep feature similarity and 40% low-level feature similarity, the results have obvious advantages. At this time, the precision is 0.745, recall is 0.850, comprehensive evaluation index is 0.794.CONCLUSIONS:We propose a similarity measure method fusing deep feature, content feature, and location feature. The retrieval results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval and location-based image retrieval.
Journal of X-Ray Science and Technology – IOS Press
Published: Feb 15, 2020
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.