Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging(MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis.Methods:
Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling(MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRIimage and evaluating the congruence of the segmented lesions.Results:
The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%).Conclusions:
The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.
The authors thank the reviewers and editorial support of Mary McAllister, MS. This work was supported in part by NIH R01CA100184, P50CA103175, 5P30CA06973, Breast SPORE P50CA88843, Avon Foundation for Women:01-2008-012, U01CA070095, and U01CA140204.
II. METHODS AND MATERIALS
II.A. Dimensionality reduction
II.B. Linear DR
II.B.2. Multidimensional scaling(MDS)
II.C. Nonlinear dimension reduction
II.C.1. Isometric feature mapping (Isomap)
II.C.2. Locally linear embedding (LLE)
II.C.3. Diffusion maps (DfM)
II.D. DR-based manifold unfolding
II.E. DR-based multidimensional image data integration
II.F. Preprocessing steps
II.F.1. Image enhancement
II.F.2. Equalizing image sizes using wavelet transform
II.F.3. Coregistration methods
II.G. Embedded image and scattergram reconstruction
II.H. Multiparametric breast MRI segmentation
II.H.1. Clinical subjects
II.H.2. Multiparametric MRIimaging protocol
II.H.3. Multiparametric breast MRI segmentation and Comparison
III.A. Synthetic data
III.B. Multiparametric breast MRI preprocessing steps
III.C. Multiparametric breast MRI segmentation
- Medical imaging
- Magnetic resonance imaging
- Medical image segmentation
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