Systems and method for automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers
View Patent ↗Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
1. A method for segmenting a prostate in Magnetic Resonance Image (MRI) data, comprising:
a processor learning a boosting classifier from a plurality of Magnetic Resonance (MR) images;
the processor determining a center of the prostate by applying the boosting classifier;
the processor applying an intensity correction to suppress an image artifact;
the processor determining a foreground seed and a background seed of the prostate based on a shape model of the prostate;
the processor applying a Random Walker segmentation method to the image data based on the foreground and background seed to generate a segmentation of the prostate; and
determining a probability threshold for the Random Walker segmentation method that maximizes a capture of a peripheral zone (PZ) in the segmentation of the prostate.
2. A method for segmenting a prostate in Magnetic Resonance Image (MRI) data, comprising:
a processor learning a boosting classifier from a plurality of Magnetic Resonance (MR) images;
the processor determining a center of the prostate by applying the boosting classifier;
the processor applying an intensity correction to suppress an image artifact;
the processor determining a foreground seed and a background seed of the prostate based on a shape model of the prostate; and
the processor applying a segmentation method to the image data based on the foreground and background seed to generate a segmentation of the prostate, wherein the foreground seed and the background seed are determined based on a GMM-EM segmentation of left and right prostate muscles and a rectum.
3. The method of claim 1 , wherein the boosted classifier is trained on intensity-based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of MR images of a prostate.
4. The method of claim 1 , further comprising:
the processor determining a signed distance map as a mean prostate shape from a plurality of prostate images.
5. The method of claim 1 , wherein the foreground seed and the background seed are determined based on a GMM-EM segmentation of left and right prostate muscles and a rectum.
6. The method of claim 1 , wherein the image artifact is created by an endorectal (ER) coil.
7. The method of claim 1 , wherein the intensity correction is an intensity transformation of a high-intensity cluster by the processor.
8. The method of claim 7 , wherein the intensity transformation is expressed as:
υ
new
=
μ
m
+
∈
+
σ
m
σ
h
(
υ
old
-
μ
h
)
,
wherein
υ new is a corrected intensity of a voxel;
υ old is an uncorrected intensity of a voxel;
μ m is a middle-intensity mean;
ε is an arbitrary constant;
σ m is a standard deviation of a middle-intensity component;
σ h is a standard deviation of a high-intensity component; and
μ h is a high-intensity mean.
9. The method as claimed in claim 1 , wherein the threshold for the Random Walker segmentation is determined from a maximum in a normalized flux.
10. A system to segment a prostate in Magnetic Resonance Image (MRI) data, comprising:
memory enabled to store data;
a processor enabled to execute instructions to perform the steps:
learning a boosting classifier from a plurality of Magnetic Resonance (MR) images;
determining a center of the prostate by applying the boosting classifier;
applying an intensity correction to suppress an image artifact;
determining a foreground seed and a background seed of the prostate based on a shape model of the prostate;
applying a Random Walker segmentation method to the image data based on the foreground and background seed to generate a segmentation of the prostate; and
determining a probability threshold for the Random Walker segmentation method that maximizes a capture of a peripheral zone (PZ) in the segmentation of the prostate.
11. The system of claim 10 , wherein the boosted classifier is trained on intensity corrected Magnetic Resonance (MR) images of a prostate.
12. The system of claim 10 , further comprising:
the processor being enabled to determine a signed distance map as a mean prostate shape from a plurality of prostate images.
13. The system of claim 10 , wherein the foreground seed and the background seed are determined based on a GMM-EM segmentation of left and right prostate muscles and a rectum.
14. The system of claim 10 , wherein the image artifact is created by an endorectal (ER) coil.
15. The system of claim 10 , wherein the intensity correction is an intensity transformation of a high-intensity cluster by the processor.
16. The system of claim 15 , wherein the intensity transformation is expressed as:
υ
new
=
μ
m
+
∈
+
σ
m
σ
h
(
υ
old
-
μ
h
)
,
wherein
υ new is a corrected intensity of a voxel;
υ old is an uncorrected intensity of a voxel;
μ m is a middle-intensity mean;
ε is an arbitrary constant;
σ m is a standard deviation of a middle-intensity component;
σ h is a standard deviation of a high-intensity component; and
μ h is a high-intensity mean.
17. The system of claim 10 , wherein the threshold for the Random Walker segmentation is determined from a maximum in a normalized flux.