Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection.

Billions of people share images from their daily lives on social media every day. However, their biometric information (e.g., fingerprints) could be easily stolen from these images. The threat of fingerprint leakage from social media has created a strong desire to anonymize shared images while maintaining image quality, since fingerprints act as a lifelong individual biometric password. To guard the fingerprint leakage, adversarial attack that involves adding imperceptible perturbations to fingerprint images have emerged as a feasible solution. However, existing works of this kind are either weak in black-box transferability or cause the images to have an unnatural appearance. Motivated by the visual perception hierarchy (i.e., high-level perception exploits model-shared semantics that transfer well across models while low-level perception extracts primitive stimuli that result in high visual sensitivity when a suspicious stimulus is provided), we propose FingerSafe, a hierarchical perceptual protective noise injection framework to address the above mentioned problems. For black-box transferability, we inject protective noises into the fingerprint orientation field to perturb the model-shared high-level semantics (i.e., fingerprint ridges). Considering visual naturalness, we suppress the low-level local contrast stimulus by regularizing the response of the Lateral Geniculate Nucleus. Our proposed FingerSafe is the first to provide feasible fingerprint protection in both digital (up to 94.12%) and realistic scenarios (Twitter and Facebook, up to 68.75%). Our code can be found at https://github.com/nlsde-safety-team/FingerSafe.

Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration.

In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.

MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer.

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that simultaneously contains functional metabolic information and structural tissue details. Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. With powerful feature representation ability, deep learning (DL)-based methods have improved such fusion results but still have not achieved satisfactory performance. Specifically, existing DL-based methods generally depend on convolutional operations, which can well extract local patterns but have limited capability in preserving global context information. To compensate for this defect and achieve accurate fusion, we propose a novel unsupervised method to fuse multimodal medical images via a multiscale adaptive Transformer termed MATR. In the proposed method, instead of directly employing vanilla convolution, we introduce an adaptive convolution for adaptively modulating the convolutional kernel based on the global complementary context. To further model long-range dependencies, an adaptive Transformer is employed to enhance the global semantic extraction capability. Our network architecture is designed in a multiscale fashion so that useful multimodal information can be adequately acquired from the perspective of different scales. Moreover, an objective function composed of a structural loss and a region mutual information loss is devised to construct constraints for information preservation at both the structural-level and the feature-level. Extensive experiments on a mainstream database demonstrate that the proposed method outperforms other representative and state-of-the-art methods in terms of both visual quality and quantitative evaluation. We also extend the proposed method to address other biomedical image fusion issues, and the pleasing fusion results illustrate that MATR has good generalization capability. The code of the proposed method is available at https://github.com/tthinking/MATR.

A weighted region-based level set method for image segmentation with intensity inhomogeneity.

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

The value of the left atrial appendage orifice perimeter of 3D model based on 3D TEE data in the choice of device size of LAmbre™ occluder.

Preoperative optimal selection of the occluder size is crucial in percutaneous left atrial appendage (LAA) occlusion, and the maximal width of the LAA orifice is the main reference index, however it can not fully meet the practical operation requirements. We retrospectively analyzed three-dimensional (3D) transesophageal echocardiography (TEE) and computed tomography (CT) imaging dataset of the 41 patients who underwent LAA occlusion with LAmbre™ system. The LAA orifice parameters were overall evaluated to determine their role in device size selection. Eight LAA 3D models of the four cases who had been replaced their device during the procedure based on TEE and CT were printed out to verify the optimal parameter decision strategy. There was a significant concordance of the results between 3D TEE and CT in the LAA orifice evaluation. The correlations between the perimeter and maximal width measurements by 3D TEE and the closure disk of the device were stronger than that between the area measurements and the closure disk (r = 0.93, 0.95, 0.86, respectively and p < 0.001 all), and the result was similar to that by CT (r = 0.92, 0.93, 0.84, respectively and p < 0.001 all). The ratios of the maximal width to the minimal width of the four cases were all > 1.4, however the rest 37 cases were all ≤ 1.4. Based on the comprehensive assessment of the LAA orifice perimeter and maximal width of the 3D printed models, the experiments were all succeed just for one try. The LAA orifice perimeter of 3D printed model based on 3D TEE may help in choosing the optimal device size of LAmbre™, especially for the LAA with flater ostial shape.

Evaluating the morphology of the left atrial appendage by a transesophageal echocardiographic 3-dimensional printed model.

The novel 3-dimensional printing (3DP) technique has shown its ability to assist personalized cardiac intervention therapy. This study aimed to determine the feasibility of 3D-printed left atrial appendage (LAA) models based on 3D transesophageal echocardiography (3D TEE) data and their application value in treating LAA occlusions.Eighteen patients with transcatheter LAA occlusion, and preprocedure 3D TEE and cardiac computed tomography were enrolled. 3D TEE volumetric data of the LAA were acquired and postprocessed for 3DP. Two types of 3D models of the LAA (ie, hard chamber model and flexible wall model) were printed by a 3D printer. The morphological classification and lobe identification of the LAA were assessed by the 3D chamber model, and LAA dimensions were measured via the 3D wall model. Additionally, a simulation operative rehearsal was performed on the 3D models in cases of challenging LAA morphology for the purpose of understanding the interactions between the device and the model.Three-dimensional TEE volumetric data of the LAA were successfully reprocessed and printed as 3D LAA chamber models and 3D LAA wall models in all patients. The consistency of the morphological classifications of the LAA based on 3D models and cardiac computed tomography was 0.92 (P < .01). The differences between the LAA ostium dimensions and depth measured using the 3D models were not significant from those measured on 3D TEE (P > .05). A simulation occlusion was successfully performed on the 3D model of the 2 challenging cases and compared with the real procedure.The echocardiographic 3DP technique is feasible and accurate in reflecting the spatial morphology of the LAA, which may be promising for the personalized planning of transcatheter LAA occlusion.

Segmentation of uterine fibroid ultrasound images using a dynamic statistical shape model in HIFU therapy.

Segmenting the lesion areas from ultrasound (US) images is an important step in the intra-operative planning of high-intensity focused ultrasound (HIFU). However, accurate segmentation remains a challenge due to intensity inhomogeneity, blurry boundaries in HIFU US images and the deformation of uterine fibroids caused by patient's breathing or external force. This paper presents a novel dynamic statistical shape model (SSM)-based segmentation method to accurately and efficiently segment the target region in HIFU US images of uterine fibroids. For accurately learning the prior shape information of lesion boundary fluctuations in the training set, the dynamic properties of stochastic differential equation and Fokker-Planck equation are incorporated into SSM (referred to as SF-SSM). Then, a new observation model of lesion areas (named to RPFM) in HIFU US images is developed to describe the features of the lesion areas and provide a likelihood probability to the prior shape given by SF-SSM. SF-SSM and RPFM are integrated into active contour model to improve the accuracy and robustness of segmentation in HIFU US images. We compare the proposed method with four well-known US segmentation methods to demonstrate its superiority. The experimental results in clinical HIFU US images validate the high accuracy and robustness of our approach, even when the quality of the images is unsatisfactory, indicating its potential for practical application in HIFU therapy.