@article{
 title = {Multimodal wavelet embedding representation for data combination (MaWERiC): Integrating magnetic resonance imaging and spectroscopy for prostate cancer detection},
 type = {article},
 year = {2012},
 keywords = {Gabor texture features,Haar wavelets,Magnetic resonance imaging,Magnetic resonance spectroscopy,Multimodal integration,PCA, random forest classifier,Prostate cancer},
 volume = {25},
 id = {41cdbc5b-f365-3fc4-ac9a-3efb851bdf8c},
 created = {2023-10-25T08:56:40.657Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:40.657Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T 2 weighted MRI (T 2w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T 2w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T 2w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5T endorectal in vivo T 2w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T 2w meta-classifier (mean AUC, μ=0.89±0.02) significantly outperformed (i) a T 2w MRI (using wavelet texture features) classifier (μ=0.55±0.02), (ii) a MRS (using metabolite ratios) classifier (μ=0.77±0.03), (iii) a decision fusion classifier obtained by combining individual T 2w MRI and MRS classifier outputs (μ=0.85±0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ=0.66±0.02). © 2011 John Wiley & Sons, Ltd.},
 bibtype = {article},
 author = {Tiwari, P. and Viswanath, S. and Kurhanewicz, J. and Sridhar, A. and Madabhushi, A.},
 doi = {10.1002/nbm.1777},
 journal = {NMR in Biomedicine},
 number = {4}
}

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However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T 2 weighted MRI (T 2w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T 2w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T 2w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5T endorectal in vivo T 2w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T 2w meta-classifier (mean AUC, μ=0.89±0.02) significantly outperformed (i) a T 2w MRI (using wavelet texture features) classifier (μ=0.55±0.02), (ii) a MRS (using metabolite ratios) classifier (μ=0.77±0.03), (iii) a decision fusion classifier obtained by combining individual T 2w MRI and MRS classifier outputs (μ=0.85±0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ=0.66±0.02). © 2011 John Wiley & Sons, Ltd.","bibtype":"article","author":"Tiwari, P. and Viswanath, S. and Kurhanewicz, J. and Sridhar, A. and Madabhushi, A.","doi":"10.1002/nbm.1777","journal":"NMR in Biomedicine","number":"4","bibtex":"@article{\n title = {Multimodal wavelet embedding representation for data combination (MaWERiC): Integrating magnetic resonance imaging and spectroscopy for prostate cancer detection},\n type = {article},\n year = {2012},\n keywords = {Gabor texture features,Haar wavelets,Magnetic resonance imaging,Magnetic resonance spectroscopy,Multimodal integration,PCA, random forest classifier,Prostate cancer},\n volume = {25},\n id = {41cdbc5b-f365-3fc4-ac9a-3efb851bdf8c},\n created = {2023-10-25T08:56:40.657Z},\n file_attached = {false},\n profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},\n last_modified = {2023-10-25T08:56:40.657Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). 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