Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 55 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,579,812 unique visits
1,024,879 downloads
Since November 1, 2009



Robots online now
Googlebot
SemanticScholar


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Application of the Voltage Control Technique and MPPT of Stand-alone PV System with Storage, HIVZIEFENDIC, J., VUIC, L., LALE, S., SARIC, M.
Issue 1/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  4/2019 - 9

The Detection and Classification of Microcalcifications in the Visibility-Enhanced Mammograms Obtained by using the Pixel Assignment-Based Spatial Filter

HEKIM, M. See more information about HEKIM, M. on SCOPUS See more information about HEKIM, M. on IEEExplore See more information about HEKIM, M. on Web of Science, AYDIN YURDUSEV, A., ORAL, C. See more information about ORAL, C. on SCOPUS See more information about ORAL, C. on SCOPUS See more information about ORAL, C. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (809 KB) | Citation | Downloads: 739 | Views: 1,948

Author keywords
biomedical image processing, cancer detection, computer aided diagnosis, mammography, spatial filters

References keywords
mammograms(13), detection(12), microcalcifications(9), microcalcification(8), image(8), system(7), digital(7), breast(7), analysis(7), segmentation(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-11-30
Volume 19, Issue 4, Year 2019, On page(s): 73 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.04009
Web of Science Accession Number: 000500274700008
SCOPUS ID: 85077265775

Abstract
Quick view
Full text preview
In this paper, we proposed a computer aided diagnosis (CAD) system which has the pixel assignment-based a spatial filter to enhance the visibility of microcalcifications in mammograms. This filter first sums the absolute values of the differences between the center pixel-of-interest and its 8-neighbors, and then assigns this summed value to that center pixel-of-interest. This process was repeated for each pixel of all images, and the contrast stretching was applied into all obtained images. Then, it was firstly detected by using different classifiers whether is absent/present of microcalcification in the obtained images, and the detected microcalcifications were classified as benign/malignant by using the same classifiers. In order to evaluate the effects of the proposed filter on the detection and classification successes, it was compared to widely used filters. In the implemented experiments, this comparison showed that the proposed filter provided higher contribution to the detection and classification successes than the others, and hence enhanced the visibility of microcalcifications in mammograms. Finally, it can be concluded that the CAD system with the proposed filter can contribute to the development of the state-of-art methodologies and can be used as a diagnostic decision support mechanism in the analysis of mammograms.


References | Cited By  «-- Click to see who has cited this paper

[1] R. Kumari, S. Venkatesh. "Breast cancer imaging techniques - A comparative study", Materials Today: Proceedings, vol. 5, no. 4, pp. 10792-10796, 2018.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[2] S. D. Desai, G. Megha, B. Avinash, K. Sudhanva, S. Rasiya, K. Linganagouda. "Detection of microcalcification in digital mammograms by improved-MMGW segmentation algorithm", Proceedings - 2013 International Conference on Cloud and Ubiquitous Computing and Emerging Technologies, CUBE 2013, pp. 213-218, 2013.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 10]


[3] P. Henrot, A. Leroux, C. Barlier, P. Genin. "Breast microcalcifications: The lesions in anatomical pathology", Diagnostic and Interventional Imaging, vol. 95, no. 2, pp. 141-152, 2014.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 64]


[4] A. Redman, S. Lowes, A. Leaver. "Imaging techniques in breast cancer", Surgery (United Kingdom), vol. 34, no. 1, pp. 8-18, 2015.
[CrossRef] [SCOPUS Times Cited 5]


[5] T. Balakumaran, I. Vennila, C. Shankar. "Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering", International Journal of Computer Science and Information Security, vol. 7, no. 1, pp. 121-125, 2010

[6] J. Dheeba, S. T. Selvi. "A swarm optimized neural network system for classification of microcalcification in mammograms", Journal of Medical Systems, vol. 36, no. 5, pp. 3051-3061, 2012.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 38]


[7] T. Balakumaran, I. L. A. Vennila, C. G. Shankar. "Microcalcification Detection in Digital Mammograms using Novel Filter", Procedia Computer Science, vol. 2, pp. 272-282, 2010.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]


[8] M. Goudarzi, K. Maghooli. "Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer", Biocybernetics and Biomedical Engineering, vol. 38, no. 4, pp. 1004-1014, 2018.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 18]


[9] E. Catanzariti, G. Forni, A. Lauria, R. Prevete, M. Santoro. "A CAD System for the Detection of Mammographyc Microcalcifications Based on Gabor Transform", IEEE Symposium Conference Record Nuclear Science 2004., vol. 6, no. C, pp. 3599-3603, 2004.
[CrossRef]


[10] M. A. Duarte, A. V. Alvarenga, C. M. Azevedo, A. F. C. Infantosi, W. C. A. Pereira. "Automatic microcalcifications segmentation procedure based on Otsu's method and morphological filters", Pan American Health Care Exchanges, PAHCE 2011 - Conference, Workshops, and Exhibits. Cooperation / Linkages: An Independent Forum for Patient Care and Technology Support, pp. 102-106, 2011.
[CrossRef] [SCOPUS Times Cited 7]


[11] S. S. Yasiran, A. K. Jumaat, A. Abdul Malek, F. H. Hashim, N. Dhaniah Nasrir, S. N. Azirah Sayed Hassan, N. Ahmad, R. Mahmud. "Microcalcifications segmentation using three edge detection techniques", 2012 IEEE International Conference on Electronics Design, Systems and Applications (ICEDSA), pp. 207-211, 2012.
[CrossRef] [SCOPUS Times Cited 15]


[12] P. Kus, I. Karagoz. "Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics", Turkish Journal of Electrical Engineering and Computer Sciences, vol. 21, no. SUPPL. 1, pp. 2048-2061, 2013.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 8]


[13] Z. Chen, H. Strange, A. Oliver, E. R. E. Denton, C. Boggis, R. Zwiggelaar. "Topological Modeling and Classification of Mammographic Microcalcification Clusters", IEEE Transactions on Biomedical Engineering, vol. 62, no. 4, pp. 1203-1214, 2015.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 72]


[14] L. Civcik, B. Yilmaz, Y. Özbay, G. D. Emlik. "Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: Automated lesion intensity enhancer (ALIE)", Turkish Journal of Electrical Engineering and Computer Sciences, vol. 23, no. 3, pp. 853-872, 2015.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


[15] S. Anand, S. Gayathri. "Mammogram image enhancement by two-stage adaptive histogram equalization", Optik, vol. 126, no. 21, pp. 3150-3152, 2015.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 47]


[16] A. H. H. Alasadi, A. K. H. Al-saedi. "A Method for Microcalcifications Detection in Breast Mammograms", Journal of Medical Systems, vol. 41, no. 68, pp. 1-9, 2017.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


[17] Y. Guo, M. Dong, Z. Yang, X. Gao, K. Wang, C. Luo, Y. Ma, J. Zhang. "A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN", Computer Methods and Programs in Biomedicine, vol. 0, no. 222, pp. 31-45, 2016.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 43]


[18] A. Abubaker. "An Adaptive CAD System to Detect Microcalcification in Compressed Mammogram Images", International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, pp. 133-138, 2017.
[CrossRef]


[19] B. Singh, M. Kaur. "An Approach for Enhancement of Microcalcifications in Mammograms", Journal of Medical and Biological Engineering, vol. 37, no. 4, pp. 567-579, 2017.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[20] V. Bhateja, M. Misra, S. Urooj. "Human visual system based unsharp masking for enhancement of mammographic images", Journal of Computational Science, vol. 21, pp. 387-393, 2017.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 35]


[21] D. Meersman, P. Scheunders, D. Van Dyck. "Detetction of Microcalcifications Using Non-linear Filtering", 9th European Signal Processing Conference, Rhodes, 1-4

[22] M. Heath, K. Bowyer, D. Kopans, R. Moore, W. P. Kegelmeyer. "The Digital Database for Screening Mammography", M. J. Yaffe (Ed.), Proceedings of the Fifth International Workshop on Digital Mammography, Medical Physics Publishing, 212-218

[23] A. Aydın Yurdusev. "The Data", from https://aysehoca.wordpress.com/the-data/

[24] M. A. Duarte, A. V Alvarenga, C. M. Azevedo, M. Julia, G. Calas, A. F. C. Infantosi, W. C. A. Pereira. "Evaluating geodesic active contours in microcalcifications segmentation on mammograms", Computer Methods and Programs in Biomedicine, vol. 122, no. 3, pp. 304-315, 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 29]


[25] P. Shi, J. Zhong, A. Rampun, H. Wang. "A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms", Computers in Biology and Medicine, vol. 96, no. March, pp. 178-188, 2018.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 77]


[26] J. R. Movellan. "Tutorial on Gabor Filters", University of California San Diago Open Source Document, 1-23

[27] J. G. Daugman. "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters", Optical Society of America, vol. 2, no. 7, pp. 1160-1169, 1985.
[CrossRef] [Web of Science Times Cited 2093] [SCOPUS Times Cited 2643]


[28] N. Petkov, P. Kruizinga. "Biological Cybernetics Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli : bar and grating cells", Biological Cybernetics, vol. 76, pp. 83-96, 1997.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 152]


[29] P. Kruizinga, N. Petkov. "Nonlinear Operator for Blob Texture Segmentation", IEEE Transactions on Image Processing, vol. 8, no. 1, pp. 881-885, 1999 http://hdl.handle.net/11370/fdc1250f-1063-4628-bc7a-13b0135f6213.

[30] B. Kamgar-Parsi, B. Kamgar-Parsi, A. Rosenfeld. "Optimum Laplacian for Digital Image Processing", International Conference on Image Processing, pp. 0-3, 1997.
[CrossRef] [Web of Science Record]


[31] S. Shaikh, A. Choudhry, R. Wadhwani. "Analysis of Digital Image Filters in Frequency Domain", International Journal of Computer Applications. vol. 140, no. 6, pp. 12-19, 2016.
[CrossRef]


[32] S. Halkiotis, T. Botsis, M. Rangoussi. "Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks", Signal Processing, vol. 87, no. 7, pp. 1559-1568, 2007.
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 94]


[33] H. S. Sheshadri, A. Kandaswamy. "Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms", Computerized Medical Imaging and Graphics, vol. 31, no. 1, pp. 46-48, 2007.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 71]


[34] I. D. Borlea, R. E. Precup, F. Dragan, A. B. Borlea. "Centroid update approach to K-means clustering", Advances in Electrical and Computer Engineering, vol. 17, no. 4, pp. 3-10, 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 17] [SCOPUS Times Cited 24]


[35] S. Chakraborty, S. Das. "K−Means clustering with a new divergence-based distance metric: Convergence and performance analysis", Pattern Recognition Letters, vol. 100, pp. 67-73, 2017.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 51]


[36] T. Zhang, F. Ma. "Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function", International Journal of Computer Mathematics, vol. 94, no. 4, pp. 663-675, 2017.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 55]


[37] R. Zall, M. R. Kangavari. "On the Construction of Multi-Relational Classifier Based on Canonical Correlation Analysis", International Journal of Artificial Intelligence, vol. 17, no. 2, pp. 23-43, 2019

[38] D. Saraswathi, E. Srinivasan. "Performance Analysis of Mammogram CAD System Using SVM and KNN Classifier", International Conference on Inventive Systems and Control, IEEE, Coimbatore, India, 1-5.
[CrossRef] [SCOPUS Times Cited 12]


[39] J. Ren. "ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging", Knowledge-Based Systems, vol. 26, pp. 144-153, 2012.
[CrossRef] [Web of Science Times Cited 194] [SCOPUS Times Cited 231]


[40] E. D. Übeyli, I. Güler. "Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides", Neurocomputing, vol. 62, nos. 1-4, pp. 349-365, 2004.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 48]


[41] I. Dalkıran, K. Danışman. "Artificial neural network based chaotic generator for cryptology", Turk J Elec Eng & Comp Sci, vol. 18, no. 2, pp. 225-240, 2010.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 36]


[42] N. Panahi, M. G. Shayesteh, S. Mihandoost, B. Zali Varghahan. "Recognition of different datasets using PCA, LDA, and various classifiers", 2011 5th International Conference on Application of Information and Communication Technologies, AICT 2011, 2011.
[CrossRef] [SCOPUS Times Cited 22]


[43] A. K. Junoh, M. N. Mansor. "Safety System Based on Linear Discriminant Analysis", International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), pp. 32-34, 2012.
[CrossRef] [SCOPUS Times Cited 4]


[44] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
[CrossRef] [Web of Science Times Cited 7848] [SCOPUS Times Cited 10155]


[45] H. Yu, J. Yang. "A direct LDA algorithm for high-dimensional data - with application to face recognition", Pattern Recognition, vol. 34, no. 10, pp. 2067-2070, 2002.
[CrossRef] [Web of Science Times Cited 1141]


[46] M. Saddique, K. Asghar, U. I. Bajwa, M. Hussain, Z. Habib. "Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames", Advances in Electrical and Computer Engineering, vol. 19, no. 3, pp. 97-108, 2019.
[CrossRef] [Full Text] [Web of Science Times Cited 19] [SCOPUS Times Cited 26]


[47] M. Hekim. "The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system", Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, no. 1, pp. 285-297, 2016.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]




References Weight

Web of Science® Citations for all references: 12,095 TCR
SCOPUS® Citations for all references: 14,149 TCR

Web of Science® Average Citations per reference: 252 ACR
SCOPUS® Average Citations per reference: 295 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-20 16:24 in 270 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy