Producing large images using small diffusion models is gaining increasing popularity, as the cost of training large models could be prohibitive. A common approach involves jointly generating a series of overlapped image patches and obtaining large images by merging adjacent patches. However, results from existing methods often exhibit obvious artifacts, e.g., seams and inconsistent objects and styles. To address the issues, we proposed Guided Fusion (GF), which mitigates the negative impact from distant image regions by applying a weighted average to the overlapping regions. Moreover, we proposed Variance-Corrected Fusion (VCF), which corrects data variance at post-averaging, generating more accurate fusion for the Denoising Diffusion Probabilistic Model. Furthermore, we proposed a one-shot Style Alignment (SA), which generates a coherent style for large images by adjusting the initial input noise without adding extra computational burden. Extensive experiments demonstrated that the proposed fusion methods improved the quality of the generated image significantly. As a plug-and-play module, the proposed method can be widely applied to enhance other fusion-based methods for large image generation.

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively.

Gland segmentation is a critical step to quantitatively assess the morphology of glands in histopathology image analysis. However, it is challenging to separate densely clustered glands accurately. Existing deep learning-based approaches attempted to use contour-based techniques to alleviate this issue but only achieved limited success. To address this challenge, we propose a novel topology-aware network (TA-Net) to accurately separate densely clustered and severely deformed glands. The proposed TA-Net has a multitask learning architecture and enhances the generalization of gland segmentation by learning shared representation from two tasks: instance segmentation and gland topology estimation. The proposed topology loss computes gland topology using gland skeletons and markers. It drives the network to generate segmentation results that comply with the true gland topology. We validate the proposed approach on the GlaS and CRAG datasets using three quantitative metrics, F1-score, object-level Dice coefficient, and object-level Hausdorff distance. Extensive experiments demonstrate that TA-Net achieves state-of-the-art performance on the two datasets. TA-Net outperforms other approaches in the presence of densely clustered glands.

This work is a cross-domain study by utilizing the most recent cloud and edge computing techniques in the human re-identification, which is a popular computer-vision application motivated by the demand of connecting and monitoring our world in the era of Internet of Things (IoT). We systematically study the real-time re-identification problem within a large-scale video surveillance network. Motivated by the system heterogeneity in terms of real-time workload and hardware configurations, we develop a workload-aware distributed system, which optimally allocates tasks across edge servers and cloud, for pursuing a user-controlled trade-off between system responsiveness & utility. We use an experiment-oriented approach to measure and model the edge heterogeneity. A two-phase task-placement algorithm is proposed which runs with the model built in the off-line phase, and driven by the dynamic real-time workload in runtime. We implement our entire system on a commercial cloud platform and use extensive simulations and experiments to validate its efficacy and responsiveness in practice.

Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality. Most tumor segmentation approaches achieve good performance on BUS images collected in controlled settings; however, the performance degrades greatly with BUS images from different sources. Tumor saliency estimation (TSE) has attracted increasing attention to solve the problem by modeling radiologists' attention mechanism. In this paper, we propose a novel hybrid framework for TSE, which integrates both high-level domain-knowledge and robust low-level saliency assumptions and can overcome drawbacks caused by direct mapping in traditional TSE approaches. The new framework integrated the Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the layer structure-based weighted map. The experimental results demonstrate that the proposed approach outperforms state-of-the-art TSE methods.

One popular approach to interactively segment an object of interest from an image is to annotate a bounding box that covers the object, followed by a binary labeling. However, the existing algorithms for such interactive image segmentation prefer a bounding box that tightly encloses the object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the bounding box only loosely covers the object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including an additional energy term to encourage consistent labeling of similar-appearance pixels and a global similarity constraint to better distinguish the foreground and background. This MRF model is then solved by an iterated max-flow algorithm. We evaluate LooseCut in three public image datasets, and show its better performance against several state-of-the-art methods when increasing the bounding-box size.

This paper proposes individualized cleaning for diverse imbalanced data sets. Existing techniques for data cleaning have difficulties with rare cases and outliers in minority class, especially, in highly unbalanced data. The drawback leads incomplete and imprecise examples to removal. In order to enhance the robustness and perform thorough data cleaning, we propose a weighted edited nearest neighbor (WENN), which detects and removes noisy examples from both classes intelligently. It considers individual characteristics of each imbalanced data, involving global class imbalance and local distribution. The main idea of the proposed method is to carefully put more focus on the majority class than the minority class during data cleaning. Extensive experiments over synthetic and real data clearly validate the superiority of our approach against other data cleaning methods.

Interactive image segmentation is a popular and challenging task. User interactions, e.g., setting seeds or specifying bounding box, play a critical role in determining the performance of all interactive segmentation approaches. However, most methods focus on improving segmentation performance by integrating higher level information; and to the best of our knowledge, no work has been done to improve the effectiveness of user interactions yet. In this paper, we propose the effective interactive segmentation (EISeg) method based on Neutro-Connectedness, which provides user with objective visual clues for guiding interactions. The experiments demonstrate that the proposed EISeg method guides interaction effectively, and achieves better results with much less user interaction (averagely 2.3 foreground and 1.8 background seeds/image) than state-of-the-art approaches.

Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than the smoothness term based on region dissimilarity when the image has complicated background or low contrast. The primal-dual interior point method is applied to optimize the proposed QP in polynomial time. Extensive experiments demonstrate that the proposed method can outperform 10 state-of-the-art methods on three public benchmark datasets.

Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches. First, the segmentation performance of region of interest (ROI)-based methods is sensitive to the initial ROI: different ROIs may produce results with great difference. Second, most seed-based methods need intense interactions, and are not applicable in many cases. In this paper, we generalize the neutro-connectedness (NC) to be independent of top-down priors of objects and to model image topology with indeterminacy measurement on image regions, propose a novel method for determining object and background regions, which is applied to exclude isolated background regions and enforce label consistency, and put forward a hybrid interactive segmentation method, NC Cut (NC-Cut), which can overcome the above two problems by utilizing both pixelwise appearance information and region-based NC properties. We evaluate the proposed NC-Cut by employing two image data sets (265 images), and demonstrate that the proposed approach outperforms the state-of-the-art interactive image segmentation methods (Grabcut, MILCut, One-Cut, MGCmaxsum, and pPBC).

Transesophageal echocardiography (TEE) is widely used to detect left atrium (LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary pattern variance (LBPV) features are extracted from region of interest (ROI). And the dynamic features are formed by using the information of its neighbor frames in the sequence. The sequence is viewed as a bag, and the ROIs in the sequence are considered as the instances. Multiple-instance learning (MIL) method is employed to solve the LAA thrombi detection. The experimental results show that the proposed method can achieve better performance than that by using other methods.

Tumor detection is the key issue of computer-aided diagnosis (CAD) systems using breast ultrasound (BUS) images. However, accurately and automatically locating the suspicious lesions in BUS images is still a very challenging job. In this paper, we propose a saliency model to describe the radiologists' visual (RV) attention. The main contributions of the paper are: (1) The saliency model is built based on biological hypotheses instead of using inflexible assumptions which are more robust, adaptive and objective. (2) Background based attention cue is also proposed. Before, the background information was either ignored or only used for excluding non-target areas. However, we find that the background information is very useful for establishing the anatomy constrains. The proposed method is evaluated using real breast ultrasound images and the result demonstrates a significantly improved performance comparing with that of the state-of-the-art methods.

This paper proposes a new Markov Random Fields (MRF) optimization model for co-segmentation. The co-saliency model is incorporated into our model to make it fully unsupervised and work well for images with similar backgrounds. The Gaussian Mixture Model (GMM) based dissimilarity between foregrounds in each image and the common objects in the set is involved as a new global constraint (i.e., energy term) in our model. Finally, we introduce an alternative approximation to represent the energy function, which could be minimized by Graph Cuts iteratively. The experimental results on two datasets show that our algorithm achieves better or comparable accuracy when comparing with state-of-the-art algorithms.

Breast tumor segmentation is an important step of breast ultrasound (BUS) computer-aided diagnosis (CAD) systems. However, because of the poor quality of BUS images, it's a challenging task to develop a robust and accurate segmentation algorithm. Much progress has been made on applying fuzzy connectedness to segment objects from low quality images. However, the fuzzy connectedness method still has difficulty in segmenting objects with weak boundaries. The neutrosophic set theory has been widely applied to image processing, and shows more strengths in modeling uncertainty and indeterminacy. In this paper, two new concepts of neutrosophic subset and neutrosophic connectedness (neutro-connectedness) were defined to generalize the fuzzy subset and fuzzy connectedness. The newly proposed neutro-connectedness models the inherent uncertainty and indeterminacy of the spatial topological properties of the image. The proposed method is applied to a breast ultrasound database with 131 cases, and its performance is evaluated by similarity ratio (SIR), false positive ratio (FPR) and average Hausdroff error (AHE). In comparison with the fuzzy connectedness segmentation method, the proposed method is more accurate and robust in segmenting tumors in BUS images.

Breast ultrasound (BUS) image segmentation is a very challenge task because of the poor image quality. In this paper, we proposed a probability model-based method for the accurate and robust segmentation for low quality medical images. It combines the spatial priori knowledge with the frequency constraints under the maximum a posteriori probability with markov random field (MAP-MRF) segmentation frameworks. The spatial constraints model the global location, object pose and the appearance, and the objective boundary is constrained in the frequency domain via modeling the phase feature and the zero crossing feature of the wavelet coefficients. The proposed method is applied to a breast ultrasound database with 131 cases, and its performance is evaluated by area error metrics and boundary error metrics. In comparing with the state of the art, our method is more accurate and robust in segmenting breast ultrasound images.