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A novel approach to microcalcification detection using fuzzy logic technique

Heng-Da Cheng, Yui Man Lui, R.I. Freimanis
1998 IEEE Transactions on Medical Imaging  
In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity.  ...  The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity  ...  ACKNOWLEDGMENT The authors wish to thank the anonymous reviewers for their invaluable comments which improve the quality of the manuscript.  ... 
doi:10.1109/42.712133 pmid:9735907 fatcat:iuvmtw6xw5aglksckhh5u72kki

Reconstruction-Independent 3D CAD for Calcification Detection in Digital Breast Tomosynthesis Using Fuzzy Particles [chapter]

G. Peters, S. Muller, S. Bernard, R. Iordache, F. Wheeler, I. Bloch
2005 Lecture Notes in Computer Science  
In this paper we present a novel approach for microcalcification detection in Digital Breast Tomosynthesis (DBT) datasets.  ...  Wavelet filter responses on the projections are thresholded and combined to obtain candidate microcalcifications.  ...  Nevertheless, an investigation on a clinical database is needed for comparing detection results to state-of-the-art 2D detection algorithms.  ... 
doi:10.1007/11578079_42 fatcat:5vspjxggefefdhaikwlbjtbf7a

Design of Primary Screening Tool for Early Detection of Breast Cancer

C. Naga Raju, C. Harikiran, T. Siva Priya
2012 Journal of Advances in Information Technology  
The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses.  ...  In the second step fuzzy logic is applied to remove ambiguity in the misclassification region and in the third step a new Weight is applied to the previously extended OTSU method.  ...  In this method fuzzy logic is applied to remove ambiguity in the misclassification region and a new Weight is applied to the previously extended OTSU method.  ... 
doi:10.4304/jait.3.4.228-235 fatcat:geppxiyldnfmrlo6wcjxrvzebm

Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images

Ayman AbuBaker
2012 The International Journal of Multimedia & Its Applications  
A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique.  ...  Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time  ...  CONCLUSIONS This paper present a novel approach to enhance the MCs in mammogram images accurately with minimum number of false positive regions using hybrid neuro-fuzzy technique.  ... 
doi:10.5121/ijma.2012.4505 fatcat:yk53xoz2vfa5hlotcrq3cihysu

Page 280 of American Society of Civil Engineers. Collected Journals Vol. 13, Issue 4 [page]

1999 American Society of Civil Engineers. Collected Journals  
A novel ap proach to microcalcification detection using fuzzy logic technique IEEE Trans. on Medical Imaging, 17(3), 442-450 heng, H. D., and Miyojim, M. (1998).  ...  CONCLUSIONS In this paper, a novel pavement cracking detection approach based on fuzzy logic is proposed.  ... 

A novel vague set approach for selective contrast enhancement of mammograms using multiresolution

Arpita Das, Mahua Bhattacharya
2009 Journal of Biomedical Science and Engineering  
The proposed algorithm introduces a novel vague set approach to develop a selective but robust, flexible and intelligent contrast enhancement technique for mammograms.  ...  After highlighting the masses/microcalcifications accurately, both LF and HF subbands are transformed back to the original resolution by inverse wavelet transform.  ...  The authors would like to thank to Dr. S. K. Sharma, Director, EKO Imaging and X-Ray Institute, Kolkata.  ... 
doi:10.4236/jbise.2009.28083 fatcat:a6vpqcw65bbu7mdadkm6heijhe

COBRA: An Evolved Online Tool for Mammography Interpretation [chapter]

Carlos-Andrés Peña-Reyes, Rosa Villa, Luis Prieto, Eduardo Sanchez
2003 Lecture Notes in Computer Science  
COBRA is designed to aid radiologists in the interpretation of mammography to decide whether to perform a biopsy on a patient or not while providing a human-friendly explanation of the underlying reasoning  ...  From a diagnostic point of view, the tool exhibits high performance measures (i.e., sensitivity, specificity, and positive predictive value).  ...  Fuzzy CoCo: A Cooperative Coevolutionary Approach to Fuzzy Modeling Fuzzy logic (which is a superset of conventional, Boolean logic) is a computational paradigm that provides a mathematical tool for representing  ... 
doi:10.1007/3-540-44868-3_92 fatcat:od5m3qfqvzgmjneqprfbaxw5zm

Detection of Microcalcification Clusters Using Hessian Matrix and Foveal Segmentation Method on Multiscale Analysis in Digital Mammograms

Balakumaran Thangaraju, Ila Vennila, Gowrishankar Chinnasamy
2012 Journal of digital imaging  
A three-phased novel approach is presented in this paper. Firstly, regions of interest that corresponds to microcalcifications are identified.  ...  Clusters of microcalcifications have been mainly targeted as a reliable early sign of breast cancer and their earliest detection is essential to reduce the probability of mortality rate.  ...  Many researchers have developed a CAD system to detect calcifications based on wavelet transform, fuzzy logic, neural networks, genetic algorithm, and adaptive thresholding techniques.  ... 
doi:10.1007/s10278-012-9489-z pmid:22581343 pmcid:PMC3447092 fatcat:usc5hb5s4vfonchokrtcnleefi

Mass Classification Method in Mammogram Using Fuzzy K-Nearest Neighbour Equality [article]

I. Laurence Aroquiaraj, K. Thangavel
2014 arXiv   pre-print
In this paper proposes a novel Fuzzy K-Nearest Neighbor Equality algorithm for classifying the marked regions into benign and malignant and 94.46 sensitivity,96.81 specificity and 96.52 accuracy is achieved  ...  Fuzzy K-Nearest Neighbor Equality exploits this important factor to classify the mass into benign or malignant.  ...  Fuzzy K-Nearest Neighbour Equality (FK-NNE) Algorithm This section proposes a novel Fuzzy K-Nearest Neighbour Equality algorithm.  ... 
arXiv:1406.4770v1 fatcat:ug6cpupcxncflhqderklja4gyq

Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images

Ayman AbuBaker, Yazeed Yasin Ghadi, Nader Santarisi
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels.  ...  In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique.  ...  ACKNOWLEDGEMENTS The author is grateful to the Applied Science Private University, Amman, Jordan, for the full financial.  ... 
doi:10.11591/ijece.v12i3.pp2564-2570 fatcat:zwzdlviybzc65p5mnngikdr64i

Review on Mammogram Mass Detection by Machine Learning Techniques

Valliappan Raman, Putra Sumari, H.H. Then, Saleh Ali K. Al-Omari
2011 International Journal of Computer and Electrical Engineering  
For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy.  ...  However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image.  ...  ACKNOWLEDGMENT The authors thank Swinburne University of technology Sarawak campus to extending their facilities to conduct preliminary experiments.  ... 
doi:10.7763/ijcee.2011.v3.436 fatcat:i2vyc4kedfahdplre3ebzybrn4

Efficient Technique to Detect the Region of Interests in Mammogram Images

Moussa H. Abdallah, Ayman A. AbuBaker, Rami S. Qahwaji, Mohammed H. Saleh
2008 Journal of Computer Science  
The development of efficient technique to early detect the region of microcalcifications mammogram images is a must.  ...  Approach: The method proposed in this paper is to enhance the Computer Aided Diagnosis (CAD) performance.  ...  MATERIALS AND METHODS Mathematical and Intelligent methods: In addition, the fuzzy logic and scale space approaches were used in detecting the microcalcification in mammogram images by Cheng [5] .  ... 
doi:10.3844/jcssp.2008.652.662 fatcat:vffqwlozznfghkuiy4xvir5hde

A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Ikhlas Abdel-Qader, Fadi Abu-Amara
2008 Modelling and Simulation in Engineering  
This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database.  ...  Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions.  ...  The authors would like also to acknowledge Western Michigan University for its support and contributions to the Information Technology and Image Analysis (ITIA) Center.  ... 
doi:10.1155/2008/238305 fatcat:n5n3kjyy2zecjhe2a3oat77kny

Sparse Representation for Detection of Microcalcification Clusters

Xinsheng Zhang, Minghu Wang
2012 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Abstract We present an approach to detect MCs in mammograms by casting the detection problem as finding sparse representations of test samples with respect to training samples.  ...  The ground truth training samples of MCs in mammograms are assumed to be known as a priori.  ...  In this paper, to detect early sign of this disease and to aid doctors to diagnose breast cancer in early stage, we propose a novel approach for classification, called sparse representation based detection  ... 
doi:10.12928/telkomnika.v10i3.309 fatcat:ldrksqv6wnfyldwp2vxtzhxb5q

Sparse Representation for Detection of Microcalcification Clusters

Xinsheng Zhang, Minghu Wang, Ji Ma Ji Ma
2012 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Abstract We present an approach to detect MCs in mammograms by casting the detection problem as finding sparse representations of test samples with respect to training samples.  ...  The ground truth training samples of MCs in mammograms are assumed to be known as a priori.  ...  In this paper, to detect early sign of this disease and to aid doctors to diagnose breast cancer in early stage, we propose a novel approach for classification, called sparse representation based detection  ... 
doi:10.12928/telkomnika.v10i3.835 fatcat:wqb6shld2naanf46eau6ug3yyy
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