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

Arpita Das, Mahua Bhattacharya
2009 Journal of Biomedical Science and Engineering  
LF subband is then fuzzified by conventional fuzzy c-means clustering (FCM) algorithm with justified number of clusters.  ...  Vague set approach captures the hesitancies and uncertainties of truly affected masses/other breast abnormalities with normal glandular tissues.  ...  Intensity Based Clustering of LF and HF Details of Input Image Using Fuzzy C-Mean Fuzzy c-means clustering is the most widely used algorithm of fuzzy classification.  ... 
doi:10.4236/jbise.2009.28083 fatcat:a6vpqcw65bbu7mdadkm6heijhe

Automatic Labelling and BI-RADS Characterisation of Mammogram Densities

K. Marias, M.G. Linguraru, M.A.G. Ballester, S. Petroudi, M. Tsiknakis, M. Brady
2005 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference  
The presented method is first concerned with the identification of the prominent structures in each mammogram.  ...  In this paper, a novel method is presented for the automatic labelling and characterisation of mammographic densities.  ...  For these reasons, there is increasing interest in using measurements of mammographic density patterns in computer-aided detection.  ... 
doi:10.1109/iembs.2005.1615961 pmid:17281731 fatcat:rj5wjzqflndkvo2iwgh6ikuwja

Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie, Mitchell M. Goodsitt
1998 Medical Physics (Lancaster)  
Features extracted from the RBST images yielded an area (A z ) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.  ...  A new rubber band straightening transform ͑RBST͒ is introduced for characterization of mammographic masses as malignant or benign.  ...  Six of the benign masses and 45 of the malignant masses were spiculated, as determined visually by a radiologist experienced in mammographic interpretation.  ... 
doi:10.1118/1.598228 pmid:9571620 fatcat:6dfn7veecjejlojvedxewok2oa

Approximate reasoning with fuzzy rule interpolation: background and recent advances

Fangyi Li, Changjing Shang, Ying Li, Jing Yang, Qiang Shen
2021 Artificial Intelligence Review  
Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with  ...  AbstractApproximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described.  ...  FRI techniques; computer vision and image super resolution (Yang et al. 2019) ; and disease diagnosis in general and mammographic mass risk analysis ) and colorectal polyp detection (Nagy et al. 2018  ... 
doi:10.1007/s10462-021-10005-3 fatcat:mfj2zm2m5vgapi2fyqx5kxxsou

Improvement of mammographic mass characterization using spiculation measures and morphological features

Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie, Lubomir M. Hadjiiski
2001 Medical Physics (Lancaster)  
of mammographic masses.  ...  The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (pϭ0.04).  ...  by a radiologist experienced in mammographic interpretation.  ... 
doi:10.1118/1.1381548 pmid:11488579 fatcat:uuek2kpfjrfapaiadynm6r6rvq

Fuzzy C-means-driven FHCE contextual segmentation method for mammographic microcalcification detection

P Bougioukos, D Glotsos, S Kostopoulos, A Daskalakis, I Kalatzis, N Dimitropoulos, G Nikiforidis, D Cavouras
2010 Imaging Science Journal  
This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE.  ...  The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.  ...  among the most difficult to interpret types of medical images.  ... 
doi:10.1179/136821909x12581187860095 fatcat:khp2cbdvyfd2ddx4tz3jivoqme

HEDGE ALGEBRAS, THE SEMANTICS OF VAGUE LINGUISTIC INFORMATION AND APPLICATION PROSPECTIVE

Nguyen Cat Ho, Tran Thai Son, Vu Nhu Lan
2016 Vietnam Journal of Science and Technology  
For illustration, we will give a short overview of effective results some of the initial applications of hedge algebras in the fields of knowledge based systems and in fuzzy control.  ...  This makes the hedge algebra based approach to the word semantics quite different from the existing approaches and become the only approach that can immediately deal with the natural qualitative semantics  ...  Figure 4 . 1 . 41 The fuzzy sets designed for the 3 thfeature of the Mammographic dataset.  ... 
doi:10.15625/0866-708x/54/1/5495 fatcat:v4itxbbsvffatlkak2xyc4f46e

Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier

Pasquale Delogu, Maria Evelina Fantacci, Parnian Kasae, Alessandra Retico
2007 Computers in Biology and Medicine  
A dataset of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses.  ...  The computer-aided diagnosis system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented  ...  Acknowledgments We would like to thank the professors, the radiologists and the employees of the Radiological Departments who contributed to the acquisition of the mammographic database in the framework  ... 
doi:10.1016/j.compbiomed.2007.01.009 pmid:17383623 fatcat:axn3hzv2c5gj5gze7vd673phci

Analysis of Oriented Texture with Applications to the Detection of Architectural Distortion in Mammograms

Fábio J. Ayres, Rangaraj M. Rangayyan, J. E. Leo Desautels
2010 Synthesis Lectures on Biomedical Engineering  
High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model.  ...  The second method incorporated the fuzzy-set theory into the regiongrowing procedure, producing a fuzzy segmentation of the masses.  ...  [136] devised two methods for the segmentation of masses using fuzzy sets.  ... 
doi:10.2200/s00301ed1v01y201010bme038 fatcat:acm7pqcz35gydnyisqbadop7my

A Novel Approach Towards Segmentation Of Breast Tumors From Screening Mammograms For Efficient Decision Support System

M.Suganthi, M.Madheswaran
2010 Zenodo  
For this reason, bilinear interpolation is used to obtain a continuous image surface on which isolevel contours are well defined.  ...  Since mammographic masses mostly have low contrast and illdefined edges, it is difficult to determine their boundary with edge-based techniques.  ... 
doi:10.5281/zenodo.1330873 fatcat:5pwdu7s4frftfh4o6pyahzogha

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
2020 Applied Sciences  
The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction.  ...  The 780 images include 133 normal images without masses, 437 images with cancer masses, and 210 images with benign masses. This set is utilized for classification, detection, and segmentation.  ...  CNN YOLO5: Fold cross-validation in both datasets; mass classification 99 93.20 78 - - 87.74 DDSM augmentation with 2.400 Mass detection 97 100 94 - - 96.45 Ragab et al.  ... 
doi:10.3390/app10228298 fatcat:3m7jxe5rjvhedhp33ryoduqxbi

Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review [article]

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
2020 arXiv   pre-print
The main findings in the classification process reveal that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction.  ...  CAD mass detection CAD mass classification 96.73 93.20 78 - - 87.74 85.52 CAD mass detection CAD mass classification 97 100 94 - - 96.45 99 Ragab et al [126] DDSM with 2620 cases  ...  Detection, segmentation and classification of masses in DM.  ... 
arXiv:2010.00961v1 fatcat:mrzh7mdlifduziuxqpokovueee

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  
Conclusions: Hence the technique proposed showed major improvement in the detection of the micro calcification and the mass region.  ...  Results: The application of the technique on 386 mammogram images from the MIAS and the USF databases showed that the method is so sensitive in detecting the microcalcifications in mammogram images with  ...  The false negative percentage was very small compared with the true positive. So, this algorithm can reasonably detect the microcalcification in mammogram images.  ... 
doi:10.3844/jcssp.2008.652.662 fatcat:vffqwlozznfghkuiy4xvir5hde

Breast Tumor Detection Via Active Contour Technique

Eman Radhi, Mustansiriyah University, Mohammed Kamil, Mustansiriyah University
2021 International Journal of Intelligent Engineering and Systems  
The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with  ...  A comparison is also made with the algorithm of M. Hmida et al. [28] , where the algorithm was a hybrid between CVM and fuzzy cmeans, they dealt with 57 mammogram images of the class of masses only.  ...  Image acquisition A mammographic image from the "Mammographic Image Analysis Society" (MIAS) which used in this research to apply the methods suggested.  ... 
doi:10.22266/ijies2021.0831.49 fatcat:uo25kzw4yjajfi3mk6sa4oifw4

DBT Masses Automatic Segmentation Using U-Net Neural Networks

Xiaobo Lai, Weiji Yang, Ruipeng Li
2020 Computational and Mathematical Methods in Medicine  
Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with  ...  To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture.  ...  [18] constructed a system of automatic detection of breast masses in DBT reconstruction images by using fuzzy theory and antagonistic reasoning method. Kim et al.  ... 
doi:10.1155/2020/7156165 pmid:32411285 pmcid:PMC7204342 fatcat:gncjacwclbgodbyvlv2lm3vnxu
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