Spatial Constraint Information Research Articles

TDG-Diff: Advancing customized text-to-image synthesis with two-stage diffusion guidance

Recently, there has been widespread attention and significant progress in customized text-to-image synthesis based on diffusion models. However, reconstructing multiple concepts in the same scene remains highly challenging. Therefore, we propose a novel framework called TDG-Diff, which employs a two-stage diffusion guidance to achieve customized image synthesis with multiple concepts. TDG-Diff focuses on improving the sampling process of the diffusion model. Specifically, TDG-Diff subdivides the sampling process into two key stages: attribute separation and appearance refinement, introducing spatial constraints and concept representations for sampling guidance. In the attribute separation stage, TDG-Diff introduces a novel attention modulation method. This method effectively separates the attributes of different concepts based on spatial constraint information, reducing the risk of entanglement between attributes of different concepts. In the appearance refinement stage, TDG-Diff proposes a fusion sampling approach, which combines global text descriptions and concept representations to optimize and enhance the model’s ability to capture and represent fine-grained details of concepts. Extensive qualitative and quantitative results demonstrate the effectiveness of TDG-Diff in customized text-to-image synthesis.

A Lidar-Assisted Self-Localization Technology for Indoor Wireless Sensor Networks

The self-localization of wireless sensor networks (WSNs) is facing the problem of insufficient positioning accuracy in indoor environment due to multipath and interference issues. At the same time, without external references, through mutual measurement nodes can calculate only a set of relative coordinates. Therefore, it is difficult to achieve the mapping of coordinate values to the physical world. The development of simultaneous localization and mapping (SLAM) technologies has provided new opportunities to solve the above problems by making it easier to obtain real-time indoor maps. This paper proposes a Lidar-assisted self-localization (LASL) technique to further improve the localization accuracy of WSNs in indoor scenes by combining spatial constraint information obtained from real-time maps, and to place the relative node coordinate network in the visualized maps. Based on the general assumption that the nodes are deployed on the surface of the object, the proposed technique combines the spatial constraints obtained by plane fitting of a local point cloud map (PCM) and finite area approximation of object surfaces with the distance constraints provided by radio ranging. Subsequently, the self-localization results under the joint constraints are solved by the alternating coordinate descent (ACD) method. Simulations and experiments demonstrate that the proposed technique can effectively combine the spatial constraints provided by the Lidar PCM to further improve the self-localization accuracy of the sensor nodes, and further optimize the relative position relationship between each node and the map environment to achieve better matching and integration of the WSN and the real-time map.

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

Sentinel-2 serves as a crucial data source for monitoring forest cover change. In this study, a sub-pixel mapping of forest cover is performed on Sentinel-2 images, downscaling the spatial resolution of the positioned results to 2.5 m, enabling sub-pixel-level forest cover monitoring. A novel sub-pixel mapping with edge-matching correction is proposed on the basis of the Sentinel-2 images, combining edge-matching technology to extract the forest boundary of Jilin-1 images at sub-meter level as spatial constraint information for sub-pixel mapping. This approach enables accurate mapping of forest cover, surpassing traditional pixel-level monitoring in terms of accuracy and robustness. The corrected mapping method allows more spatial detail to be restored at forest boundaries, monitoring forest changes at a smaller scale, which is highly similar to actual forest boundaries on the surface. The overall accuracy of the modified sub-pixel mapping method reaches 93.15%, an improvement of 1.96% over the conventional Sub-pixel-pixel Spatial Attraction Model (SPSAM). Additionally, the kappa coefficient improved by 0.15 to reach 0.892 during the correction. In summary, this study introduces a new method of forest cover monitoring, enhancing the accuracy and efficiency of acquiring forest resource information. This approach provides a fresh perspective in the field of forest cover monitoring, especially for monitoring small deforestation and forest degradation activities.

Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model

Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients' recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper-acute phase of stroke. In this paper, a two-stage convolutional neural network-based method was proposed to identify the invisible ischemic stroke from ncCT. In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end-to-end U-net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet-based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information. Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method. There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two-stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke.

Early identification of ischemic stroke in noncontrast computed tomography

Early identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect the early ischemic stroke that is invisible to the radiologist in ncCT. First, we proposed radiomics-based patch classification model to identify whether each patch in ncCT is stroke region or not. To improve the identification accuracy, a symmetry image patch classification was developed, in which an image patch in one brain hemisphere was combined with its contralateral image patch for classification. Second, based on the spatial dependencies between neighboring patches, we built a maximum a posteriori identification model which integrated the spatial constraint information to improve the identification performance. Finally, after performing morphological post-processing, we defined the detected results containing more than 300 voxels as the stroke region. 108 stroke cases that were all invisible to radiologists in ncCT were enrolled in the study and divided into one training cohort and two independent testing cohorts to validate the proposed method. The proposed method has achieved identification accuracies of 76.67% and 75.00% on the two independent testing cohorts, respectively. The results proved the potential of the proposed radiomics model in the task which possessed great clinical values.

Sparse group composition for robust left ventricular epicardium segmentation

Left ventricular (LV) epicardium segmentation in cardiac magnetic resonance images (MRIs) is still a challenging task, where the a-priori knowledge like those that incorporate the heart shape model is usually used to derive reasonable segmentation results. In this paper, we propose a sparse group composition (SGC) approach to model multiple shapes simultaneously, which extends conventional sparsity-based single shape prior modeling to incorporate a-priori spatial constraint information among multiple shapes on-the-fly. Multiple interrelated shapes (shapes of epi- and endo-cardium of myocardium in the case of LV epicardium segmentation) are regarded as a group, and sparse linear composition of training groups is computed to approximate the input group. A framework of iterative procedure of refinement based on SGC and segmentation based on deformation model is utilized for LV epicardium segmentation, in which an improved shape-constraint gradient Chan-Vese model (GCV) acted as deformation model. Compared with the standard sparsity-based single shape prior modeling, the refinement procedure has strong robust for relative gross and not much sparse errors in the input shape and the initial epicardium location can be estimated without complicated landmark detection due to modeling spatial constraint information among multiple shapes effectively. Proposed method was validated on 45 cardiac cine-MR clinical datasets and the results were compared with expert contours. The average perpendicular distance (APD) error of contours is 1.50±0.29mm, and the dice metric (DM) is 0.96±0.01. Compared to the state-of-the-art methods, our proposed approach appealed competitive segmentation performance and improved robustness.

An improved extrapolation method and its application in ultrasound computerized tomography

In ultrasound computer tomography, according to Fourier diffraction theorem, when an object illuminated with an ultrasound wave, the Fourier transform of the forward scatter field gives values of 2-D transform of the object along semicircular arcs in the frequency domain.We can reconstruct the object with data sampled from these arcs. However, these values are all limited in a low frequency band.Aiming this problem, a new extrapolation scheme was proposed in this paper. Papoulis-Gerchberg (G-P) algorithm is first applied, using spatial constraint information iterative to get the initial information in high frequency, then an improved method of nonlinear extrapolation is applied to enhance the high frequency part. The simulation results show the new method largely improves reconstruction quality.

Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory

Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.