A major challenge to facial de-identification in 3D brain MR images is to find a trade-off between patient privacy protection and retaining the usefulness of the image data. An efficient facial de-identification method is proposed. The method can efficiently conceal identifiable facial details in the 3D brain MR images while maintaining the usefulness of the data. The experimental results indicated the proposed method can achieve the state-of-the-art performance and retain more image data in comparison with the currently available tools.
A four-stage procedure is employed to conceal subject’s identity in a novel 3D brain MR image. (1) The image is registered into the same stereotactic space $$$U$$$ with the training images using mutual information based image registration [5][6]. (2) Key-point based deformable models are used to detect subject’s face, as shown (Fig.1). (3) A line searching method is used to extract the superficial surface of the subject’s face (Fig.2). (4) Finally, a surface editing procedure is carried out to obscure subject’s facial details (Fig.3).
To train the key-point based deformable model from $$$t$$$-th training image $$$I_{t}$$$. The facial regions $$$F_{t}$$$ in $$$I_{t}$$$ is delineated by a trained expert (Fig.1a). A group of key points are extracted from the facial region $$$F_{t}$$$ using [7][8](Fig.1b). Here we assume the bounding box that encloses the facial region in $$$I_{t}$$$ is denoted as $$$B_{t}$$$. A deformable face detection model $$$M_{i}=\left\{k;L;U\right\}=\left\{k_{1},k_{2}...;L_{1},L_{2},...;U_{1},U_{2},...\right\}$$$ can be trained from $$$I_{t}$$$. Here, $$$k_{j}$$$ is the spatial position of $$$j$$$-th key-point in $$$I_{t}$$$, $$$L_{j}$$$ is the relative displacement between $$$j$$$-th key-point and the lowest point of $$$B_{t}$$$. $$$H_{j}$$$ is the relative displacement between $$$j$$$-th key-point and the upper most point of $$$B_{t}$$$(Fig.1b). To use $$$M_{i}$$$ to detect subject’s face in a novel image, we first match the key points in the novel image, and using the relative displacement between each key-point and the facial bounding box as a prior knowledge to determine the possible bounding box position in that image (Fig.1c). Multiple deformable models and the multi-criterion decision making method [9][10] are used to fuse the detection results for facial bounding box in the novel image (Fig.2a).
A line searching method is used to identify the superficial surface of subject's face (Fig.2b). The image volume within $$$B^{'}$$$ was segmented into facial region $$$F$$$ and non-facial region $$$NF$$$. A line searching was performed at each voxel position in $$$F$$$, along the direction $$$\overrightarrow{l}$$$ which points from posterior to anterior in the normalized space $$$U$$$. The position of the superficial point $$$P_{s}$$$ on subject's face $$$F_{s}$$$ can be determined by the position of the last intersecting point between the searching line and the regional boundary of $$$F$$$ (Fig.2b).
To obscure the facial details on $$$F_{s}$$$, a surface editing procedure is carried out. $$$F_{s}$$$ was down-sampled by skipping every 3 voxels. The sub-sampled facial surface was denoted as $$$D_{s}$$$. In the cubic regions around each vertex on $$$D_{s}$$$ (Fig.3d), image voxel values were set to zero to obscure subject’s facial details.
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