Patent Document ID: 9269156
Application ID: 13947300

Base Claim:
1. A method for automatic prostate segmentation in magnetic resonance (MR) image data, comprising: performing a first intensity normalization on the MR image to adjust for global contrast changes between the MR image and other MR scans; performing a second intensity normalization on the MR image to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image by: obtaining a mask image from the MR image using intensity thresholding, extracting a bright region from the MR image using the mask image, and calculating adjusted intensities to reduce an overall intensity within the bright region such that the adjusted intensities at a boundary of the bright region match a surrounding region in the MR image and gradient features within the bright region are retained; obtaining an initial prostate segmentation in the MR image by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL); and refining the initial prostate segmentation using one or more trained boundary classifiers.

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Claim 5:
5. The method of claim 1 , wherein performing the second intensity normalization on the MR image to adjust for intensity variation within the MR image due to the endorectal coil used to acquire the MR image comprises: obtaining the mask image as M=((I>τ 1 )⊕B) (I>τ 2 ), where I is the MR image, τ 1 and τ 2 are intensity thresholds, τ 1 >τ 2 , and ⊕B is a dilation with a circular ball; extracting the bright region Ω R 2 from the MR image Ω R 2 , as the non-zero elements of the mask image M; generating a high pass filtered image as g(x)=(I−G σ *I)(x), where G σ is a Gaussian function; and calculating adjusted intensities within the bright region f: Ω R as E(f)=min∫ Ω |∇f−∇g| 2 dx where f=I on δΩ by solving a Poisson equation: ∇ 2 f=∇ 2 g.