Motion correction and normalization of features in optical coherence tomography
An optical coherence tomography system is provided. The system includes an OCT imager; a two-dimensional transverse scanner coupled to the OCT imager, the two-dimensional transverse scanner receiving light from the light source and coupling reflected light from a sample into the OCT imager; a computer coupled to receive 3D OCT data from the OCT imager, the computer further processes the 3D OCT data; wherein the processing the 3D OCT data includes: correcting motion artifacts in baseline mode; generating reference data in baseline mode; performing segmentation to identify volumes of interest; extracting feature information, the feature information including reflectivity, texture, or the combination thereof.
1. A method of data analysis for ophthalmology, comprising:
acquiring first 3D OCT data;
correcting motion artifacts in the first 3D OCT data;
extracting a networking structure of blood vessels from the first 3D OCT data;
saving the networking structure of the blood vessels as reference data, the reference data using less storage space than would otherwise be used to store the first 3D OCT data;
acquiring second 3D OCT data for processing in a follow-up mode, the second 3D OCT data being acquired at a different point in time from the first 3D OCT data and from a different imaging modality than the first 3D OCT data;
registering the second 3D OCT data to the saved reference data;
performing segmentation of the first or second 3D OCT data to identify volumes of interest;
extracting feature information from the first or second 3D OCT data, the feature information including reflectivity, texture, or a combination thereof.
2. The method of claim 1 , further comprising normalizing the feature information.
3. The method of claim 2 , further comprising constructing a normative database and identifying abnormality in the first or second 3D OCT data.
4. The method of claim 1 , further comprising performing progression analysis using the first and second 3D OCT data.
5. The method of claim 1 , wherein the different imaging modality is chosen from a set that includes color fundus photography, visual field testing, scanning laser ophthalmoscope, fluorescein angiography, indocyanine green angiography, and ultrasonography.
6. The method of claim 1 , wherein the volume of interest includes data substantially from a retinal region, a choroidal region, a corneal region, or a combination thereof.
7. The method of claim 6 , wherein the volume of interest contains information from at least one spatial dimension.
8. The method of claim 1 , further comprising calculating a quantitative parameter, the quantitative parameter being selected from a group comprising an intensity level distribution measure, a run length measure, a co-occurrence matrix measure, a wavelet analysis measure, a pattern spectrum measure, and a combination thereof.
9. The method of claim 1 , wherein the feature information further includes a tissue shape-size spectrum, tissue entropy measure, tissue contrast or a combination thereof.
10. An optical coherence tomography (OCT) system, comprising:
an OCT imager;
a two-dimensional transverse scanner coupled to the OCT imager, the two-dimensional transverse scanner receiving light from the light source and coupling reflected light from a sample into the OCT imager; and
a computer coupled to receive first and second 3D OCT data from the OCT imager, the second 3D OCT data having been acquired at a different point in time from the first 3D OCT data and from a different imaging modality than the first 3D OCT data, the computer further capable of executing instructions to process the first and second 3D OCT data, the instructions including:
correcting motion artifacts in the first 3D OCT data;
extracting a networking structure of blood vessels from the first 3D OCT data;
saving the networking structure of the blood vessels as reference data, the reference data using less storage space than would otherwise be used to store the first 3D OCT data;
registering the second 3D OCT data to the saved reference data;
performing segmentation of the first or second 3D OCT data to identify volumes of interest; and
extracting feature information from the first or second 3D OCT data, the feature information including reflectivity, texture, or a combination thereof.
11. The OCT system of claim 10 , the instructions further comprising normalizing the feature information, constructing a normative database, and identifying abnormality in the first or second 3D OCT data.
12. The OCT system of claim 10 , the instructions further comprising performing progression analysis with using the first and second 3D OCT data.
13. The OCT system of claim 10 , wherein the different imaging modality is chosen from a set that includes color fundus photography, visual field testing, scanning laser ophthalmoscope, fluorescein angiography, indocyanine green angiography, and ultrasonography.
14. The OCT system of claim 10 , wherein the volume of interest includes data substantially from a retinal region, a choroidal region, a corneal region, or a combination thereof; and contains information from at least one spatial dimension.
15. The OCT system of claim 10 , the instructions further comprising calculating a quantitative parameter, the quantitative parameter being selected from a group comprising an intensity level distribution measure, a run length measure, a co-occurrence matrix measure, a wavelet analysis measure, a pattern spectrum measure, and a combination thereof.
16. The OCT system of claim 10 , wherein the feature information further includes a tissue shape-size spectrum, tissue entropy measure, tissue contrast or a combination thereof.