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Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering
TLDR
The proposed algorithm for detecting microcalcification in mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microCalcification detection by fuzzy shell clustering and the effectiveness of the proposed algorithm is confirmed by experimental results.Abstract:
Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.read more
Citations
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Journal ArticleDOI
Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer
TL;DR: The usage of NN in four different medical imaging applications is discussed to show that NN is not restricted to few areas of medicine, and hybrid NN adaptation in breast cancer detection is addressed.
Journal ArticleDOI
Fusion of local and global features for classification of abnormality in mammograms
Anuradha C. Phadke,Priti P. Rege +1 more
TL;DR: A novel computer aided technique to classify abnormalities in mammograms using fusion of local and global features that has improved classification accuracy from 88.75% to 93.17%.
Proceedings ArticleDOI
A systematic algorithm for 3-D reconstruction of MRI based brain tumors using morphological operators and bicubic interpolation
TL;DR: This paper involves implementing various steps of extracting the tumor from the 2D slices of MRI brain images by OTSUs threshold technique and various morphological operations and designing software for reconstructing 3D image from a set of 2D tumor images.
Journal ArticleDOI
Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications
TL;DR: The results showed that there was a strong consistency between the estimated and the actual number of TPs (or FPs) for these detectors, and lesions estimated to be more accurate in detection were shown to have better classification accuracy than those estimates to be less accurate.
Breast Cancer Detection Using Support Vector Machine Technique Applied on Extracted Electromagnetic Waves
M. Al Sharkawy,Maha Sharkas,Dina Ahmed Ragab,V. P. McGinn,Veysel Demir,D. Yagyu,M. Taguchi,S. E. Hosseininejad,N. Komjani,H. Oraizim,M. T. Noghani,M. Agatonovic,Z. Marinkovic,V. Markovic,S. H. Yusop,Norbahiah Misran,M. T. Islam,M. Y. Ismail,Y. Li,W. Li,W. Yu,M. Mighani,M. Akbari,N. Felegari,L. He,L. Lang,Q. Li,W. Zheng,W. Abdelli,X. Mininger,L. Pichon,H. Trabelsi +31 more
TL;DR: A new CAD system for the detection of breast cancer in mammograms is proposed using electromagnetic waves instead of the classical MRI approach, which classifies normal and abnormal tissues in addition to benign and malignant tumors.
References
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Wavelet transforms for detecting microcalcifications in mammograms
R.N. Strickland,Hee Il Hahn +1 more
TL;DR: A 2-stage method based on wavelet transforms for detecting and segmenting calcifications designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries is developed.
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A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films
Songyang Yu,Ling Guan +1 more
TL;DR: A computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms gives quite satisfactory detection performance.
Journal ArticleDOI
A novel approach to microcalcification detection using fuzzy logic technique
TL;DR: 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 fuzzification image with the original image to preserve fidelity.
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