Min Xian

Unsupervised co-segmentation based on a new global GMM constraint in MRF

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.

Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound 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.

A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness

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, its 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.

Neutro-Connectedness Cut

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).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).

Automatic Breast Ultrasound Image Segmentation: A Survey

Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages.

EISeg: Effective interactive segmentation

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.

Robust multiple cue fusion-based high-speed and nonrigid object tracking algorithm for short track speed skating

Abstract. This paper presents a methodology for tracking multiple skaters in short track speed skating competitions. Nonrigid skaters move at high speed with severe occlusions happening frequently among them. The camera is panned quickly in order to capture the skaters in a large and dynamic scene. To automatically track the skaters and precisely output their trajectories becomes a challenging task in object tracking. We employ the global rink information to compensate camera motion and obtain the global spatial information of skaters, utilize random forest to fuse multiple cues and predict the blob of each skater, and finally apply a silhouette- and edge-based template-matching and blob-evolving method to labelling pixels to a skater. The effectiveness and robustness of the proposed method are verified through thorough experiments.

Unsupervised saliency estimation based on robust hypotheses

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.

An algorithm based on LBPV and MIL for left atrial thrombi detection using transesophageal echocardiography

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.

WENN for individualized cleaning in imbalanced data

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.