Accurate and efficient vertebrae localization and detection in X-ray images are essential for diagnosing and treating spinal diseases. However, most existing methods struggle with the complexity of spine X-ray images, yielding inaccurate results due to insufficient utilization of spinal anatomy information and neglect of individual vertebra characteristics. In this paper, we propose an innovative Vertebrae Localization and Detection Network (VLD-Net) to accurately assist physicians in diagnosing spine-related diseases from X-ray images. Our VLD-Net, for the first time, defines vertebrae localization as a top-bottom sequential decision-making process, employing deep reinforcement learning (DRL) to fully leverage the anatomical information of the spine. Simultaneously, it also prioritizes the distinct characteristics of each vertebra for accurate detection. Specifically, VLD-Net combines three key components: 1) An advanced vertebrae localization module based on DRL is proposed, effectively leveraging anatomical information of the spine. 2) A novel adaptive exploration mechanism is coined to understand the behavior of the DRL agent during training, pinpointing how to effectively achieve the trade-off between exploration and exploitation. 3) An innovative vertebra-focused module is proposed to accurately detect vertebral landmarks, using the attention region of each vertebra as input to enhance focus on the target and reduce interference from surrounding tissue. Extensive experiments on two public spine datasets demonstrate that the VLD-Net outperforms the state-of-the-art methods in accuracy and robustness.

Visible-infrared person re-identification (VI-ReID) task is to retrieve the same pedestrian across the visible and infrared modalities. The existing transformer-based works are constrained by the inherent structure of the ViT that feature collapse in deeper layers and the over-globalization of extracted features, resulting in incomplete learning of local and low-level features. However, these features are instrumental in representing and identifying elements within visible-infrared images more comprehensively, which increases the accuracy and robustness of cross-modal pedestrian matching. To solve the above problem, we propose the Local-Aware Residual Attention Vision Transformer (LAReViT) to enhance the learning of fine-grained local and shallow-level information to reinforce the feature discrimination and comprehensiveness in ViT. Specifically, the Local-Aware Residual (LAR) Module, which uses a novel Local Residual Attention (LRA) mechanism, is proposed to increase the fine-grained local information contained in feature extraction. In order to exploit fine-grained local information lost in lower-level visual features, the LRA in the LAR module adopts novel attention residual connections. Additionally, we propose a Positional Channel Reconstruction (PCR) Module that takes advantage of the local receptive field benefits of convolution. PCR reweights features within patches at the channel level, further facilitating the network emphasis on effective fine-grained local information. Finally, the novel Center Aggregation Loss (CAL) is designed to reduce modality discrepancies moderately and promote comprehensive feature extraction. Extensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets demonstrate the state-of-the-art performance achieved by our proposed method. The code is available at https://github.com/Hua-XC/LAReViT .

Accurate and automatic delineation of the left atrium (LA) is crucial for computer-aided diagnosis of atrial fibrillation-related diseases. However, effective model training typically requires a large amount of labeled data, which is time-consuming and labor-intensive. In this study, we propose a novel LA delineation framework. The region of LA is first detected using an actor-critic based deep reinforcement learning method with a shape-adaptive detection strategy using only box-level annotations, bypassing the need for voxel-level labeling. With the effectively detected LA, the impacts of class-imbalance and interference from surrounding tissues are significantly reduced. Subsequently, a semi-supervised segmentation scheme is coined to precisely delineate the contour of LA in 3D volume. The scheme integrates two independent networks with distinct structures, enabling implicit consistency regularization, capturing more spatial features, and avoiding the error accumulation present in current mainstream semi-supervised frameworks. Specifically, one network is combined with Transformer to capture latent spatial features, while the other network is based on pure CNN to capture local features. The difference prediction between these two sub-networks is exploited to mutually provide high-quality pseudo-labels and correct the cognitive bias. Experimental results on two public datasets demonstrate that our proposed strategy outperforms several state-of-the-art methods in terms of accuracy and clinical convenience.

Active contour model (ACM) plays an important role in computer vision and medical image analysis. The traditional ACMs were employed to extract closed contours of objects. While simultaneous extraction of line- and block-like objects, such as boundary contours, centerlines, as well as their topological relationship, remains open so far. Therefore, a novel ACM named "Ingenious Snake" is proposed to adaptively extract the feature curves. The proposed ingenious snake includes the following steps: 1) In the preprocessing, the line- and block-like structures are classified with k-means clustering, following up with morphological operation and enhancement. The gradient vector flow (GVF) field is then acquired from the resultant ridge feature map. 2) For the automatic initialization, the ridge-points are extracted by using the local phase measurement of GVF field, then the two-category of object ridgelines are obtained fast. 3) Finally, the contour deformation and curves evolvement are implemented with a management strategy. The resultant contours and centerlines well characterize the objects of interest. In the experiments, we compare the existing initialization methods and the adaptive extraction with a series of phantoms and testing images. The visual and quantitative assessments of structure extraction are satisfying in terms of effectiveness and accuracy.

Compressive Tracking (CT) model is a recently proposed method for visual tracking, in which the appearance model is constructed from the features selected from the multiscale image feature space based on compressive sensing. The CT tracker has been proven to be effective. However, since it does not discriminatively consider the sample importance in its learning procedure, the CT tracker may detect the less important positive samples and, therefore, suffer from drift. In this paper, we present a novel Weighted Compressive Tracking (WCT) model based on the CT tracker. The proposed WCT tracker integrates the sample importance into an efficient online learning procedure so that the features are much more discriminative. Experimental results on challenging benchmark image sequences demonstrate that the proposed WCT tracker performs more favorably than the CT tracker. In addition, the WCT and CT trackers are also applied to the video acquired by the fisheye lens, the result of WCT tracker is very promising, whereas the CT tracker fails.

Computation of the optical flow from a sequence of images remains open in the community of computer vision. Two classical models for this problem are the global smoothness algorithm proposed by Horn-Schunck and the oriented smoothness algorithm by Nagel and Enkelmann. In order to increase the accuracy of motion discontinuity, we propose a new optical flow model which incorporates the harmonic smoothness constraint borrowed from the harmonic gradient vector flow (HGVF) model into the oriented smoothness constraint. In particular, we combine the curl term of the harmonic constraint with the oriented smoothness to control the direction of the displacement vectors together and introduce two spatially varying weighting functions to control the above-mentioned two terms. The benefit of the suggested strategies is illustrated qualitatively on the synthetical image and quantitatively on the Middlebury optical flow benchmark. Compared with the classical Horn-Schunck and Nagel-Enkelmann methods, this method can provide more accurate estimation of optical flow around motion discontinuities.

Active contours have been one of the most successful methods for image segmentation during the last two decades, but one of the shortcomings of being unable to converge to concavity is a handicap to its effectiveness. In order to address this issue, the gradient vector flow (GVF) was put forth. Although there have been a great number of works on GVF, the image structure has seldom been incorporated into GVF algorithm. In this work, the image structure characterized by the Hessian matrix is incorporated into the GVF algorithm by reformulating the smoothness constraint of GVF into matrix form. In this way, the associated diffusion PDEs are anisotropic and the modified GVF snake can converge to very long concavity and preserve weak edge simultaneously. Experiments and comparisons are presented to demonstrate the properties of the proposed strategies.

The Chan-Vese model is one of the most popular region-based active contours, and its vector-valued extension is also powerful for multichannel images. Very recently, the histogram is introduced into the Chan-Vese model due to the effectiveness of histogram to model region information. Motivated by the fact that the histogram is also a powerful tool to characterize texture, it is introduced into the vector-valued Chan-Vese model for texture segmentation in this work. In order to determine an optimal number of bins in the histogram, a Bayesian method is adopted. Experiments are conducted and the results show that the proposed strategy is effective for texture segmentation.

This paper proposes a novel external force for active contours, called adaptive diffusion flow (ADF). We reconsider the generative mechanism of gradient vector flow (GVF) diffusion process from the perspective of image restoration, and exploit a harmonic hyper surface minimal function to substitute smoothness energy term of GVF for alleviating the possible leakage problem. Meanwhile, a ∞- laplacian functional is incorporated in the ADF framework to ensure that the vector flow diffuses mainly along normal direction in homogenous regions of an image. Experiments on synthetic and real images demonstrate the good properties of the ADF snake, including noise robustness, weak edge preserving, and concavity convergence.

Gradient vector flow (GVF) is an effective external force for active contours, but its isotropic nature handicaps its performance. The recently proposed NGVF model is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it is sensitive to noise and could erase weak boundaries. In this letter, the normally biased GVF (NBGVF) external force is proposed for snake models, which keeps the diffusion along the tangential direction of the isophotes and biases that along the normal direction. The biasing weight approaches zero at boundaries and is 1 in homogeneous regions. Consequently, the NBGVF snake can preserve weak edges and smooth out noise while maintaining other desirable properties of GVF and NGVF snakes such as enlarged capture range, insensitivity to initialization and convergence to u-shape concavity. These properties are evaluated on synthetic and real images.

Magnetic resonance imaging is a noninvasive method to measure the geometry and deformation of the heart during one cardiac cycle. To make thorough use of this anatomical and functional information, it is necessary to segment the endo- and epicardium of the left ventricle. In this study, we present a method based on GVF snake model for this purpose. For endocardium segmentation, the proposed method pays particular attention to papillary muscle and artifacts by adopting a shape energy, with this energy, the snake contour can overcome the spurious edges stemming from artifacts and the final results could depend much less on the initial contour. In order to segment the epicardium, a novel energy based on shape similarity is proposed by assuming that the epicardium resembles the endocardium in shape. In addition, a new strategy is developed to derive the external force. This new force can push the snake contour directly to the epicardium when using the endocardium as initialization. By applying the proposed method to a set of 140 cardiac images, its accuracy and robustness is demonstrated and validated.