Journal Article10.1118/1.598273
Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms
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TL;DR: A technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of the CAD scheme based on the supervised learning method is developed.
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Abstract: We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.
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Citations
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review.
Afsaneh Jalalian,Syamsiah Mashohor,Hajjah Rozi Mahmud,M. Iqbal Saripan,Abdul Rahman Ramli,Babak Karasfi +5 more
TL;DR: The approaches which are applied to develop CAD systems on mammography and ultrasound images are presented and the performance evaluation metrics of CAD systems are reviewed.
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Computer-Aided Detection and Diagnosis in Mammography
Mehul Sampat,Mia K. Markey,Alan C. Bovik +2 more
- 01 Dec 2005
TL;DR: A novel evidence based, stage-one algorithm for the detection of spiculated masses (SM) and architectural distortions (AD) and the fundamental issue of whether using data from multiple views can really provide additional insight to the diagnosis problem is addressed.
Fast detection of masses in computer-aided mammography
TL;DR: This method can distinguish between tumorous and healthy tissue among various parenchyma tissue patterns, making a decision whether a mammogram is normal or not, and then detecting the masses' position by performing sub-image windowing analysis.
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Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques
TL;DR: In this work, the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection system for the detection of microcalcifications in mammograms is assessed.
144
An evaluation of contrast enhancement techniques for mammographic breast masses
Sameer Singh,K. Bovis +1 more
- 01 Mar 2005
TL;DR: It is shown that the quantitative measures help to select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.
116
References
Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
TL;DR: The potential application of such a computer-aided system to mammographic interpretation is demonstrated by its ability to detect microcalcifications in clinical mammograms.
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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.
393
Artificial convolution neural network for medical image pattern recognition
TL;DR: Radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts and an unconventional method of using rotation and shift invariance is proposed to enhance the neural net performance.
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Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.
TL;DR: It was found that the neural networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false-positive clusters of microCalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme.
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An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms
TL;DR: Modifications were made to improve the performance of the SIANN and the zero-mean-weight constraint and training-free-zone techniques have been developed and a cross-validation training method was also applied to avoid the overtraining problem.
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