# US 2019/0378607 A1 · System and method for patient privacy protection in medical images

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2019/0378607 A1
- Document ID: US20190378607A1
- Status: Published application
- Kind: A1
- Application number: 16/006,663
- Filed: 2018-06-12
- Published: 2019-12-12
- Earliest claimed: 2018-06-12
- Assignees: The Chinese University of Hong Kong
- Applicants: The Chinese University of Hong Kong
- Inventors: Weitian CHEN; Ke GAN
- Web page: https://patentgrep.com/patent/US20190378607A1

## CPC

- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G06F 21/6245: {Protecting personal data, e.g. for financial or medical purposes}
- G06F 21/6254: {by anonymising data, e.g. decorrelating personal data from the owner's identification}
- G06T 5/20: using local operators
- G06T 5/70: Denoising; Smoothing
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 7/155: involving morphological operators
- G06T 7/194: involving foreground-background segmentation
- G06T 19/20: Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 30/40: for processing medical images, e.g. editing
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/30004: Biomedical image processing
- G06T 2219/2021: Shape modification
- G06V 40/53: {Measures to keep reference information secret, e.g. cancellable biometrics}
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and methods for de-identification of medical images can be applied to medical images acquired using various techniques. A 3D medical image can be analyzed to generate an image mask that partitions the image into a foreground region and a background region. From the image mask, a “skin surface” can be reconstructed based on the boundary between the foreground region and the background region. The image mask can be modified, e.g., by moving a randomly-selected subset of the voxels from the foreground region to the background region so that the shape of the skin surface is altered, thus obscuring patient-identifying features. The original medical image can be modified by changing the intensity of voxels in the background region while preserving the original intensity of voxels in the foreground region.

## Claims

### Claim 1

A method of de-identifying a medical image, the method comprising:
obtaining medical image data representing anatomy of a patient, the medical image data including a set of voxels defined in a three-dimensional space, each voxel having an original intensity value;
analyzing the medical image data to generate an image mask that assigns each of the voxels to either a foreground region or a background region such that a skin surface at a boundary between the foreground region and the background region corresponds to one or more surface anatomical features of the patient;
modifying the image mask by moving a randomly selected subset of voxels from the foreground region to the background region such that the skin surface is reshaped; and
modifying the medical image data by assigning a uniform background intensity value to each voxel of the medical image data that is assigned to the background region of the modified image mask while preserving the original intensity values of each voxel of the medical image data that is assigned to the foreground region of the modified image mask.

### Claim 2 (depends on 1)

The method of claim 1 wherein analyzing the medical image data to generate the image mask includes:
identifying a largest connected region of low intensity voxels as belonging to a first approximate background region of a first approximate image mask; and
identifying all other voxels as belonging to a first approximate foreground region of the first approximate image mask.

### Claim 3 (depends on 2)

The method of claim 2 wherein analyzing the medical image data to generate the image mask further includes:
applying a morphological correction to the first approximate background region; and
modifying the first approximate image mask based on the morphological correction to produce a second approximate image mask that divides the voxels into a second approximate foreground region and a second approximate background region.

### Claim 4 (depends on 3)

The method of claim 3 wherein analyzing the medical image to generate the image mask further includes:
reconstructing an approximate skin surface from the second approximate image mask;
identifying a set of super-voxels around the approximate skin surface; and
for each super-voxel:
determining whether the super-voxel includes at least a threshold number of voxels that are within the second approximate foreground region;
assigning all voxels within the super-voxel to a refined foreground region of a refined image mask in the event that the super-voxel includes at least the threshold number of voxels that are within the second approximate foreground region; and
assigning all voxels within the super-voxel to a refined background region of the refined image mask in the event that the super-voxel does not include at least the threshold number of voxels that are within the second approximate foreground region.

### Claim 5 (depends on 1)

The method of claim 1 wherein modifying the image mask includes:
randomly selecting a plurality of seed locations on the skin surface of the image mask; and
for each seed location, applying a kernel at the seed location to select one or more voxels near the seed location to be moved from the foreground region to the background region of the image mask.

### Claim 6 (depends on 5)

The method of claim 5 wherein the same kernel is applied at each seed location.

### Claim 7 (depends on 6)

The method of claim 6 wherein the kernel is a spherical kernel.

### Claim 8 (depends on 5)

The method of claim 5 wherein modifying the image mask further includes:
applying iterative Gaussian smoothing to propagate a deformation at each seed location.

### Claim 9 (depends on 1)

The method of claim 1 wherein the medical image data is data produced from a magnetic resonance imaging (MM) scan of the patient.

### Claim 10

A computer system comprising:
a storage medium to store medical image data representing anatomy of a patient, the medical image data including a set of voxels defined in a three-dimensional space, each voxel having an original intensity value; and
a processor coupled to the storage medium and configured to:
analyze the medical image data to generate an image mask that assigns each of the voxels to either a foreground region or a background region such that a skin surface at a boundary between the foreground region and the background region corresponds to one or more surface anatomical features of the patient;
modify the image mask by moving a randomly selected subset of voxels from the foreground region to the background region such that the skin surface is reshaped; and
modify the medical image data by assigning a uniform background intensity value to each voxel of the medical image data that is assigned to the background region of the modified image mask while preserving the original intensity values of each voxel of the medical image data that is assigned to the foreground region of the modified image mask.

### Claim 11 (depends on 10)

The computer system of claim 10 wherein analyzing the medical image data to generate the image mask includes:
identifying a largest connected region of low intensity voxels as belonging to a first approximate background region of a first approximate image mask; and
identifying all other voxels as belonging to a first approximate foreground region of the first approximate image mask.

### Claim 12 (depends on 11)

The computer system of claim 11 wherein analyzing the medical image data to generate the image mask further includes:
applying a morphological correction to the first approximate background region; and
modifying the first approximate image mask based on the morphological correction to produce a second approximate image mask that divides the voxels into a second approximate foreground region and a second approximate background region.

### Claim 13 (depends on 12)

The computer system of claim 12 wherein analyzing the medical image to generate the image mask further includes:
reconstructing an approximate skin surface from the second approximate image mask;
identifying a set of super-voxels around the approximate skin surface; and
for each super-voxel:
determining whether the super-voxel includes at least a threshold number of voxels that are within the second approximate foreground region;
assigning all voxels within the super-voxel to a refined foreground region of a refined image mask in the event that the super-voxel includes at least the threshold number of voxels that are within the second approximate foreground region; and
assigning all voxels within the super-voxel to a refined background region of the refined image mask in the event that the super-voxel does not include at least the threshold number of voxels that are within the second approximate foreground region.

### Claim 14 (depends on 10)

The computer system of claim 10 wherein modifying the image mask includes:
randomly selecting a plurality of seed locations on the skin surface of the image mask; and
for each seed location, applying a kernel at the seed location to select one or more voxels near the seed location to be moved from the foreground region to the background region of the image mask.

### Claim 15 (depends on 14)

The computer system of claim 14 wherein the same kernel is applied at each seed location.

### Claim 16 (depends on 15)

The computer system of claim 15 wherein the kernel is a spherical kernel.

### Claim 17 (depends on 14)

The computer system of claim 14 wherein modifying the image mask further includes:
applying iterative Gaussian smoothing to propagate a deformation at each seed location.

### Claim 18 (depends on 10)

The computer system of claim 10 wherein the medical image data is data produced from a magnetic resonance imaging (MRI) scan of the patient.

### Claim 19

A computer-readable storage medium having stored therein program instructions that, when executed by a processor of a computer system, cause the processor to execute a method comprising:
obtaining medical image data representing anatomy of a patient, the medical image data including a set of voxels defined in a three-dimensional space, each voxel having an original intensity value;
analyzing the medical image data to generate an image mask that assigns each of the voxels to either a foreground region or a background region such that a skin surface at a boundary between the foreground region and the background region corresponds to one or more surface anatomical features of the patient;
modifying the image mask by moving a randomly selected subset of voxels from the foreground region to the background region such that the skin surface is reshaped; and
modifying the medical image data by assigning a uniform background intensity value to each voxel of the medical image data that is assigned to the background region of the modified image mask while preserving the original intensity values of each voxel of the medical image data that is assigned to the foreground region of the modified image mask.

### Claim 20 (depends on 19)

The computer-readable storage medium of claim 19 wherein analyzing the medical image data to generate the image mask includes:
identifying a largest connected region of low intensity voxels as belonging to a first approximate background region of a first approximate image mask; and
identifying all other voxels as belonging to a first approximate foreground region of the first approximate image mask.

### Claim 21 (depends on 20)

The computer-readable storage medium of claim 20 wherein analyzing the medical image data to generate the image mask further includes:
applying a morphological correction to the first approximate background region; and
modifying the first approximate image mask based on the morphological correction to produce a second approximate image mask that divides the voxels into a second approximate foreground region and a second approximate background region.

### Claim 22 (depends on 21)

The computer-readable storage medium of claim 21 wherein analyzing the medical image to generate the image mask further includes:
reconstructing an approximate skin surface from the second approximate image mask;
identifying a set of super-voxels around the approximate skin surface; and
for each super-voxel:
determining whether the super-voxel includes at least a threshold number of voxels that are within the second approximate foreground region;
assigning all voxels within the super-voxel to a refined foreground region of a refined image mask in the event that the super-voxel includes at least the threshold number of voxels that are within the second approximate foreground region; and
assigning all voxels within the super-voxel to a refined background region of the refined image mask in the event that the super-voxel does not include at least the threshold number of voxels that are within the second approximate foreground region.

### Claim 23 (depends on 19)

The computer-readable storage medium of claim 19 wherein modifying the image mask includes:
randomly selecting a plurality of seed locations on the skin surface of the image mask; and
for each seed location, applying a kernel at the seed location to select one or more voxels near the seed location to be moved from the foreground region to the background region of the image mask.

### Claim 24 (depends on 23)

The computer-readable storage medium of claim 23 wherein the same kernel is applied at each seed location.

### Claim 25 (depends on 24)

The computer-readable storage medium of claim 24 wherein the kernel is a spherical kernel.

### Claim 26 (depends on 23)

The computer-readable storage medium of claim 23 wherein modifying the image mask further includes:
applying iterative Gaussian smoothing to propagate a deformation at each seed location.

### Claim 27 (depends on 19)

The computer-readable storage medium of claim 19 wherein the medical image data representing anatomy of a patient is obtained from a data storage medium of the computer system.

### Claim 28 (depends on 19)

The computer-readable storage medium of claim 19 wherein the medical image data representing anatomy of a patient is obtained by performing a scan of the patient.

### Claim 29 (depends on 28)

The computer-readable storage medium of claim 28 wherein the scan is a magnetic resonance imaging (MRI) scan.