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A novel vague set approach for selective contrast enhancement of mammograms using multiresolution
2009
Journal of Biomedical Science and Engineering
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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
2005
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
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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
1998
Medical Physics (Lancaster)
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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
2021
Artificial Intelligence Review
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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
2001
Medical Physics (Lancaster)
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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
2010
Imaging Science Journal
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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
2016
Vietnam Journal of Science and Technology
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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
2007
Computers in Biology and Medicine
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2008 pre-print arXiv:0803.1417v1 fatcat:4da3gsmue5d6xp4w2dybo4wxgq
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
2010
Synthesis Lectures on Biomedical Engineering
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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
2010
Zenodo
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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
2020
Applied Sciences
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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]
2020
arXiv
pre-print
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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
2008
Journal of Computer Science
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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
2021
International Journal of Intelligent Engineering and Systems
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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
2020
Computational and Mathematical Methods in Medicine
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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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