| 1. |
- Gal, Yaniv, et al.
(författare)
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A new denoising method for dynamic contrast-enhanced MRI
- 2008
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Ingår i: Proc. 2008 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 9781424418145 ; , s. 847 - 850
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. The algorithm is called Dynamic Non-Local Means and is a novel variation on the Non-Local Means (NL-Means) algorithm. It exploits the redundancy of information in the DCE-MRI sequence of images. An evaluation of the performance of the algorithm relative to six other denoising algorithms—Gaussian filtering, the original NL-Means algorithm, bilateral filtering, anisotropic diffusion filtering, the wavelets adaptive multiscale products threshold method, and the traditional wavelet thresholding method—is also presented. The evaluation was performed by two groups of expert observers—18 signal/image processing experts, and 9 clinicians (8 radiographers and 1 radiologist)—using real DCE-MRI data. The results of the evaluation provide evidence, at the α=0.05 level of significance, that both groups of observers deem the DNLM algorithm to perform visually better than all of the other algorithms.
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| 2. |
- Gal, Yaniv, et al.
(författare)
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An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast
- 2007
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Ingår i: Proc. 2007 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 9781424407873 ; , s. 71 - 74
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Konferensbidrag (refereegranskat)abstract
- This paper presents an empirical evaluation of the goodness-of-fit (GOF) of four parametric models of contrast enhancement for dynamic resonance imaging of the breast: the Tofts, Brix, and Hayton pharmacokinetic models, and a novel empiric model. The goodness-of-fit of each model was evaluated with respect to: (i) two model-fitting algorithms (Levenberg- Marquardt and Nelder-Mead) and two fitting tolerances; and (ii) temporal resolution. In the first case the GOF was measured using data from three dynamic contrast-enhanced (DCE) MRI data sets from routine clinical examinations: one case with benign enhancement, one with malignant enhancement, and one with normal findings. Results are presented for fits to both the whole breast volume and to a selected region of interest. In the second case the GOF was measured by first fitting the models to several temporally sub-sampled versions of a custom high temporal resolution data set (subset of the breast volume containing a malignant lesion), and then comparing the fitted results to the original full temporal resolution data. Our results demonstrate that under the various optimization conditions considered, in general, both the proposed empiric model and the Hayton model fit the data equally well and that both of these models fit the data better than the Tofts and Brix models.
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| 3. |
- Gal, Yaniv, et al.
(författare)
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Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images
- 2007
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Ingår i: Proc. 2007 Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA). - 0769530672 ; , s. 124-129
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.
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| 4. |
- Gal, Yaniv, et al.
(författare)
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Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast
- 2009
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Ingår i: Proc. 2009 International Conference on Digital Image Computing: Techniques and Applications (DICTA). - 9781424452972 ; , s. 132 - 139
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Konferensbidrag (refereegranskat)abstract
- The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).
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| 5. |
- Gal, Yaniv, et al.
(författare)
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Mutual information-based binarisation of multiple images of an object: An application in medical imaging
- 2013
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Ingår i: IET Computer Vision. - : Institution of Engineering and Technology (IET). - 1751-9640 .- 1751-9632. ; 7:3, s. 163-169
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Tidskriftsartikel (refereegranskat)abstract
- A new method for image thresholding of two or more images that are acquired in different modalities or acquisition protocols is proposed. The method is based on measures from information theory and has no underlying free parameters nor does it require training or calibration. The method is based on finding an optimal set of global thresholds, one for each image, by maximising the mutual information above the thresholds while minimising the mutual information below the thresholds. Although some assumptions on the nature of images are made, no assumptions are made by the method on the intensity distributions or on the shape of the image histograms. The effectiveness of the method is demonstrated both on synthetic images and medical images from clinical practice. It is then compared against three other thresholding methods.
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| 6. |
- Gal, Yaniv, et al.
(författare)
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New Spatiotemporal Features for Improved Discrimination of Benign and Malignant Lesions in Dynamic Contrast-Enhanced-Magnetic Resonance Imaging of the Breast
- 2011
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Ingår i: Journal of Computer Assisted Tomography. - 1532-3145 .- 0363-8715. ; 35:5, s. 645-652
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast.Methods: A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features.Results: Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06.Conclusions: Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy.
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| 7. |
- Hill, Andrew, et al.
(författare)
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A fast, segmentation-free, method for constructing a biomechanical model of the breast from DCE-MRI data
- 2008
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Ingår i: Proc. 2008 International Conference on Digital Image Computing: Techniques and Applications (DICTA). - 9780769534565 ; , s. 386 - 391
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for constructing a biomechanical breast model which does not require an initial time- and labour-intensive segmentation of the breast tissue. This is achieved by mapping voxel intensity and enhancement levels directly to Youngpsilas modulus values characteristic of the respective tissues. We demonstrate this new method by incorporating it into a biomechanically based registration evaluation framework, which produces qualitatively the same results as a segmentation based model.
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| 8. |
- Hill, Andrew, et al.
(författare)
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Dynamic breast MRI: Image registration and its impact on enhancement curve estimation
- 2006
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Ingår i: Proc. 2006 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 1424400325 ; , s. 3049 - 3052
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Konferensbidrag (refereegranskat)abstract
- A novel algorithm for performing registration of dynamic contrast-enhanced (DCE) MRI data of the breast is presented. It is based on an algorithm known as iterated dynamic programming originally devised to solve the stereo matching problem. Using artificially distorted DCE-MRI breast images it is shown that the proposed algorithm is able to correct for movement and distortions over a larger range than is likely to occur during routine clinical examination. In addition, using a clinical DCE-MRI data set with an expertly labeled suspicious region, it is shown that the proposed algorithm significantly reduces the variability of the enhancement curves at the pixel level yielding more pronounced uptake and washout phases
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| 9. |
- Hill, Andrew, et al.
(författare)
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Edge intensity normalization as a bias field correction during balloon snake segmentation of breast MRI
- 2008
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Ingår i: Proc. 2008 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 1557-170X. ; , s. 3040 - 3043
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Konferensbidrag (refereegranskat)abstract
- Segmentation of fat suppressed dynamic contrast enhanced MRI (DCE-MRI) image data can pose significant problems because of the inherently poor signal-to-noise ratio (SNR) and intensity variations due to the bias field. Segmentation methods such as balloon snakes, while able to operate in a poor SNR environment, are sensitive to variations in edge intensity, which are regularly encountered within DCE-MRI due to the bias field. In order to overcome the effects of the bias field, an intensity normalization based on the strength of the strongest edge, i.e. the skin-air-boundary, is proposed and evaluated. This normalization allows balloon segmentations to be run three times faster while maintaining, or even improving accuracy.
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| 10. |
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