Computer assisted diagnosis (CAD) of cancer using multi-functional, multi-modal in-vivo magnetic resonance spectroscopy (MRS) and imaging (MRI)
AssigneesThe Trustees of the University of PA., Rutgers, The State University of New Jersey
InventorsMichael D. Feldman, Satish Viswanath, Pallavi Tiwari, Robert Toth, Anant Madabhushi, John Tomaszeweski, Mark Rosen
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Abstract
This invention relates to computer-assisted diagnostics and classification of prostate cancer. Specifically, the invention relates to segmentation of the prostate boundary on MRI images, cancer detection using multimodal multi-protocol MR data; and their integration for a computer-aided diagnosis and classification system for prostate cancer.
Claims21
A method of constructing a classifier to detect cancer sub-clusters in an organ using a dataset containing in vivo high resolution magnetic resonance imaging (MRI) data, comprising the steps of:
- a. preprocessing the MRI data by correcting bias field inhomogeneity and non-linear MRI artifacts to create a corrected MRI scene;
- b. rigidly or non-rigidly registering determined correspondences of whole-mount histological sections (WMHS) and MRI data via a Combined Feature Ensemble Mutual Information (COFEMI) technique, thereby obtaining cancerous and non-cancerous regions in the preprocessed MRI data;
- c. extracting features on a per-voxel basis from the MRI scene;
- d. embedding the extracted feature into a low dimensional Eigen space using manifold learning techniques on a per-voxel basis, thereby non-linearly reducing the dimensionality of the extracted image features;
- e. training a classifier on the reduced dimensional Eigen feature to discriminate a voxel from an MRI scene as cancerous or normal;
- and
- f. classifying each voxel in the MRI scene based on its reduced dimensional Eigen feature as cancerous or normal, thereby detecting cancer.
A method of combining multiple magnetic resonance imaging protocols to detect cancer sub-clusters in an organ using a dataset containing in vivo high resolution MRI data, comprising the steps of:
- a. preprocessing the MRI data by correcting bias field inhomogeneity and non-linear MR artifacts for each MRI protocol to create corrected multi-protocol MRI scenes;
- b. rigidly or non-rigidly registering determined correspondences of whole-mount histological sections (WMHS) and multi-protocol MRI scenes via a Combined Feature Ensemble Mutual Information (COFEMI) technique, thereby obtaining cancerous and non-cancerous regions on multi-protocol MRI data;
- c. extracting features on a per-voxel basis from each multi-protocol MRI scene;
- d. embedding each extracted multi-protocol MRI scene feature into a low dimensional Eigen space using manifold learning techniques on a per-voxel basis, thereby non-linearly reducing the dimensionality of the extracted multi-protocol MRI scene feature;
- e. concatenating low dimensional Eigen vectors from each MRI protocol scene on a per-voxel basis, thereby combining features of the reduced dimensional multi-protocol MRI scene;
- f. training a classifier on the combined reduced dimensional Eigen feature to discriminate a voxel from a multi-protocol MRI scene as cancerous or normal;
- and
- g. classifying each voxel in the multi-protocol MRI scene based on its reduced dimensional Eigen feature as cancerous or normal, thereby detecting cancer.
A method of combining multiple magnetic resonance imaging protocols to identify high grade cancer sub-clusters in an organ using a dataset containing in vivo high resolution MRI data, comprising the steps of:
- a. preprocessing the MRI data by correcting bias field inhomogeneity and non-linear MRI artifacts to create a corrected MRI scene;
- b. rigidly or non-rigidly registering determined correspondences of whole-mount histological sections (WMHS) and MRI data via a Combined Feature Ensemble Mutual Information (COFEMI) technique, thereby obtaining cancerous and non-cancerous regions in the MRI data;
- c. extracting features on a per-voxel basis from the MRI scene;
- d. embedding the extracted feature into a low dimensional Eigen space using manifold learning techniques on a per-voxel basis, thereby non-linearly reducing the dimensionality of the extracted image features;
- e. concatenating low dimensional Eigen vectors from each MRI protocol scene on a per-voxel basis, thereby combining features of the reduced dimensional multi-protocol MRI scene;
- f. training a classifier on the combined reduced dimensional Eigen feature to discriminate a voxel from an MRI scene as high- or low-grade cancer;
- and
- g. classifying each voxel in the MRI scene based on its reduced dimensional Eigen feature as high- or low-grade, thereby identifying high-grade cancer.
Dependencies are inferred from the claim text.