Interactive Image Segmentation Method Research Articles

An Interactive Image Segmentation Method in Hand Gesture Recognition.

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

Interactive image segmentation based on samples reconstruction and FLDA

Existing interactive image segmentation methods heavily rely on manual input, i.e. a sufficient quantity and correct locations of labels. In this paper, we propose a new interactive segmentation algorithm which aims to reduce human intervention and to generate high-quality segmentation results. In contrast to most energy minimizing based segmentation methods, the segmentation is cast as multi-classification in our proposed method. First, the input image is segmented into superpixels by using different methods. Then we build a dictionary consisting of all obtained superpixels and reconstruct samples represented by certain labeled superpixels. Finally, we learn a discriminative projection matrix through Fishers linear discriminant analysis (FLDA) algorithm, which learns a discriminative subspace for classification. The unlabeled superpixels are grouped into foreground or background, via calculating their minimal norm. Our method can capture long range grouping cues and reduce the sensitivity with respect to input label quantity and location of labels, by the combination of superpixels and discriminative dictionary. Extensive experiments are conducted both on MSRC and another challenging database in order to demonstrate the effectiveness of the proposed method. Quantitative and qualitative results show that our method is competitive to the state-of-the-art performance.

An interactive image segmentation method for lithological boundary detection: A rapid mapping tool for geologists

Large volumes of images are collected by geoscientists using remote sensing platforms. Manual analysis of these images is a time consuming task and there is a need for fast and robust image interpretation tools. In particular the reliable mapping of lithological boundaries is a critical step for geological interpretation. In this contribution we developed an interactive image segmentation algorithm that harnesses the geologist's input and exploits automated image analysis to provide a practical tool for lithology boundary detection, using photographic images of rock surfaces.In the proposed method, the user is expected to draw rough markings to indicate the locations of different geological units in the image. Image segmentation is performed by segmenting regions based on their homogeneity in colour. This results in a high density of segmented regions which are then iteratively merged based on the colour of different geological units and the user input. Finally, a post-processing step allows the user to edit the boundaries.An experiment was conducted using photographic rock surface images collected by a UAV and a handheld digital camera. The proposed technique was applied to detect lithology boundaries. It was found that the proposed method reduced the interpretation time by a factor of four relative to manual segmentation, while achieving more than 96% similarity in boundary detection. As a result the proposed method has the potential to provide practical support for interpreting large volume of complex geological images.

Interactive Multilabel Image Segmentation via Robust Multilayer Graph Constraints

The combination of pixel and superpixel has been widely utilized in the interactive segmentation methods to overcome the sensitivity to the seeds’ quantity and quality. However, because of the introduction of more variables and variables’ interactions, the pixel–superpixel combination methods are still limited to the segmentation accuracy and computational complexity. To solve these problems, in this paper, we propose an interactive multilabel image segmentation method. In the proposed segmentation model, the multilayer relationships among the pixel layer, superpixel layer, and label layer are fused by the Markov random field framework to further improve the segmentation accuracy. During the optimization stage, the parallel partial optimality strategy is utilized to effectively solve the multilabel submodular energy function. Experimental results on challenging data sets demonstrate the competitiveness of the proposed method comparing with several state-of-the-art interactive algorithms.

Diffusion map based interactive image segmentation

Image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. In this paper, a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods.

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).

Extracted Pixels Similarity Features (EPSF) using Interactive Image Segmentation Techniques

automatic image segmentation methods are among the segmentation methods that are used to achieve high quality segmentation result. These techniques are said to be interactive because their processes required input from their users to distinguish between the foreground and background of the image via color makers. Therefore color and texture features are an important aspect for the improvement of those methods in order to achieve success. The reason is that, the addition of a vast range of color features can guide the region merging process to achieve accurate results. In this paper, we proposed a new interactive image segmentation method based on extracted pixels similarity features with the aid of color parameters comprising of seven features. These are namely: Red, Green, Blue, Hue, Saturation, Value and Texture Features. In our method, the initial step is to pre-segment the image. The next is the extraction of the color features from the image. Finally our method then merge all the pre-segmented regions of the image background to extract the contour. Our proposed method has successfully been implemented and achieve good quality segmentation results by measuring the similarity between a region and its neighboring regions. In this method, the image regions are acquired by means of a pre-segmentation process via mean-shift. The region merging process was carried out based on the highest similarity between regions. Thus, the regions were merged with their neighboring regions based on the fact that the highest similarity criteria was achieved. From the various experiments that were performed on the nine test images, it is evident that our proposed method achieved an average of 98.99% from all experiment carried out on the nine test images, when compared with their ground truth. That shows that our proposed method produced better segmentation results than the Graph Cut and the Dimension- free Directional Filter Bank Thresholding and Multistage Adaptive Thresholding respectively. Finally, our proposed method have been proved to be robust enough to segment both color and greyscale images and based on the results produced it can be concluded that our proposed method have achieved good quality segmentation results for both color and greyscale images.

Multi-layer graph constraints for interactive image segmentation via game theory

The combination of pixels and superpixels has been widely used for image segmentation, where the pixels and superpixels are segmented together. These combination methods can obtain more robust results by using more informative superpixel features. However, since the superpixel may not accurately capture the details for the small and slender regions, the results of these combination methods are often label inconsistent with the objects. Furthermore, these methods also fall into expensive time cost due to introducing more interactions between pixels and superpixels. To overcome the above problems, in this paper, we propose an interactive image segmentation method based on multi-layer graph constraints. The relationships between pixels/superpixels and labels are introduced into the conventional combination framework to further improve the segmentation accuracy. The segmentation model is constructed based on the estimation of probabilities of pixels and superpixels by a nonparametric learning framework. Then the probabilities of pixels and superpixels are updated iteratively by utilizing the game theory based optimization strategy. Experiments on challenging data sets demonstrate that the proposed method can obtain better segmentation results than the state-of-the-art methods.

An efficient two-stage region merging method for interactive image segmentation

Interactive image segmentation aims to extract user-specified regions from the background. In this paper, an efficient two-stage region merging based method is proposed for interactive image segmentation. An image is first over-segmented into many super-pixels using a bottom-up method. The color histogram is exploited to represent each super-pixel, and the Bhattacharyya coefficient is computed to measure the similarity of two adjacent super-pixels. Then some strokes, denoting the desired object and background, are manually labeled by the user on the over-segmented image. With the labeled seed super-pixels, a merging strategy is designed to realize adaptive region merging. The whole merging process is divided into two stages, which are repeatedly executed until no new merging occurs. In the first stage, some unlabelled super-pixels are merged into the labeled foreground or background super-pixels if the labeled ones are their nearest neighbors. In the second stage, any two unlabelled super-pixels are merged together if one super-pixel is the nearest neighbor of the other. Extensive experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method can extract the object reliably and quickly from the background.

Robust foreground object segmentation from handheld camera videos with occlusion map

Extracting foreground objects from videos captured by a handheld camera has emerged as a new challenge. While existing approaches aim to exploit several clues such as depth and motion to extract the foreground layer, there are limitations in handling partial movement and cast shadow. In this paper, we bring a novel perspective to address these two issues by utilizing occlusion map introduced by object and camera motion and taking the advantage of interactive image segmentation methods. For partial movement, we treat each video frame as an image and synthesize "seeding" user interactions (i.e., user manually marking foreground and background) with both forward and backward occlusion maps to leverage the advances in high quality interactive image segmentation. For cast shadow, we utilize a paired region based shadow detection method to further refine initial segmentation results by removing detected shadow regions. Experimental results from both qualitative evaluation and quantitative evaluation on the Hopkins dataset demonstrate both the effectiveness and the efficiency of our proposed approach.

Volume Estimation of Various Brain Components Using MR Images - A Technical Report

In this paper, we present and discuss issues related to volume estimation of various brain components using MR brain images. We discuss pre-processing techniques to remove elements such as skull, blood vessels and fats from the MR images since these are non-essential to the volume calculations. The volume estimation is based on image segmentation. A challenge in MR brain image segmentation is to distinguish central gray matter from surrounding matters. This paper provides a frame work from pre-processing stage through volume estimation for white matter, gray matter and cerebrospinal fluid (CSF). The main contribution of this paper contains two parts. First, it provides a software-based method for interactive image pre-processing; second, it introduces a software-based, supervised interactive image segmentation method to deal with the segmentation of different matters, especially the central gray matter. Experiments with real data demonstrate the efficiency of our frame work.

An Interactive Segmentation Method Based on Superpixel

This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors' method has excellent per- formance in terms of segmentation accuracy and computation efficiency compared with other segmentation algo- rithm based on pixels.

Hybrid approaches for interactive image segmentation using the live markers paradigm.

Interactive image segmentation methods normally rely on cues about the foreground imposed by the user as region constraints (markers/brush strokes) or boundary constraints (anchor points). These paradigms often have complementary strengths and weaknesses, which can be addressed to improve the interactive experience by reducing the user’s effort. We propose a novel hybrid paradigm based on a new form of interaction called live markers, where optimum boundary-tracking segments are turned into internal and external markers for region-based delineation to effectively extract the object. We present four techniques within this paradigm: 1) LiveMarkers; 2) RiverCut; 3) LiveCut; and 4) RiverMarkers. The homonym LiveMarkers couples boundary-tracking via live-wire-on-the-fly (LWOF) with optimum seed competition by the image foresting transform (IFT-SC). The IFT-SC can cope with complex object silhouettes, but presents a leaking problem on weaker parts of the boundary that is solved by the effective live markers produced by LWOF. Conversely, in RiverCut, the long boundary segments computed by Riverbed around complex shapes provide markers for Graph Cuts by the Min-Cut/Max-Flow algorithm (GCMF) to complete segmentation on poorly defined sections of the object’s border. LiveCut and RiverMarkers further demonstrate that live markers can improve segmentation even when the combined approaches are not complementary (e.g., GCMFs shrinking bias is also dramatically prevented when using it with LWOF). Moreover, since delineation is always region based, our methodology subsumes both paradigms, representing a new way of extending boundary tracking to the 3D image domain, while speeding up the addition of markers close to the object’s boundary-a necessary but time consuming task when done manually. We justify our claims through an extensive experimental evaluation on natural and medical images data sets, using recently proposed robot users for boundary-tracking methods.

Interactive Medical Image Segmentation Method with only Positive Label Examples

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

Interactive color image segmentation via iterative evidential labeling

We develop an interactive color image segmentation method in this paper. This method makes use of the conception of Markov random fields (MRFs) and D–S evidence theory to obtain segmentation results by considering both likelihood information and priori information under Bayesian framework. The method first uses expectation maximization (EM) algorithm to estimate the parameter of the user input regions, and the Bayesian information criterion (BIC) is used for model selection. Then the beliefs of each pixel are assigned by a predefined scheme. The result is obtained by iteratively fusion of the pixel likelihood information and the pixel contextual information until convergence. The method is initially designed for two-label segmentation, however it can be easily generalized to multi-label segmentation. Experimental results show that the proposed method is comparable to other prevalent interactive image segmentation algorithms in most cases of two-label segmentation task, both qualitatively and quantitatively.

Interactive segmentation algorithm optimized by multi-threshold with application in medical images

Interactive image segmentation methods usually ask users to mark much more initial seeds or more than one interaction when they are used for medical image segmentation with fuzzy boundaries.This paper presented an optimized interactive image segmentation algorithm with multi-threshold technology.Based on GorwCut algorithm put forward by Vladimir,the optimized algorithm introduced image gray histogram with more than one threshold values to generate initial seeds template automatically and then used improved cellular automaton iterative algorithm to realize image segmentation.The algorithm simplified the user interactive operations and improved the segmentation accuracy.In applications,the algorithm was used to test on 100 plaque and liver image segmentations respectively,of which the results show that the optimized algorithm is of good performance.

SimLocator: robust locator of similar objects in images

Similar objects commonly appear in natural images, and locating and cutting out these objects can be tedious when using classical interactive image segmentation methods. In this paper, we propose SimLocator, a robust method oriented to locate and cut out similar objects with minimum user interaction. After extracting an arbitrary object template from the input image, candidate locations of similar objects are roughly detected by distinguishing the shape and color features of each image. A novel optimization method is then introduced to select accurate locations from the two sets of candidates. Additionally, a matting-based method is used to improve the results and to ensure that all similar objects are located in the image. Finally, a method based on alpha matting is utilized to extract the precise object contours. To ensure the performance of the matting operation, this work has developed a new method for foreground extraction. Experiments show that SimLocator is more robust and more convenient to use compared to other more advanced repetition detection and interactive image segmentation methods, in terms of locating similar objects in images.

TH-C-WAB-02: Interactive Segmentation of Structures in the Head and Neck Using Steerable Active Contours

Purpose: The purpose of this work is to investigate the performance of an interactive image segmentation method for radiotherapy contouring on computed tomography (CT) images. Manual segmentation is a time consuming task that is essential for treatment. Due to the low contrast of target structures, their similarity to surrounding tissue, and the required precision for the final segmentation Result, automatic methods do not exhibit robust performance. Furthermore, when an automatic segmentation algorithm produces errors at the structure boundary, they are tedious for a human user to correct. For this experiment, it is hypothesized that an interactive algorithm can attain ground truth results in a fraction of the the time needed for manual segmentation. Methods: The proposed method is interactive segmentation that tightly couples a human “expert user” with a framework from computer vision called “active contours” to create a closed loop control system. As a Result, the strengths (i.e., quickly delineating complicated target boundaries) of the automatic method can be leveraged by the user, who guides the algorithm based on his expert knowledge throughout the process. Experimental segmentations have been performed both with and without the control system feedback, the accuracy of the resulting labels will be compared along with the time required to create the labels. Results: Four structures were evaluated: left/right eye ball, brain stem, and mandible. Tests show that virtually identical segmentations are performed with and without control system feedback. However, the time required to complete the task is significantly less than what is needed for fully manual contouring. Conclusion: Interactive segmentation using control system feedback is shown to reduce the time and effort needed to segment targets in CT volumes of the head and neck region. This project was supported by grants from the National Center for Research Resources (P41-RR-013218) and the National Institute of Biomedical Imaging and Bioengineering (P41-EB-015902) of the National Institutes of Health. This work was also supported by the NIH grant R01 MH82918 as well as a grant from AFOSR. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.

Learning from Only Positive and Unlabeled Examples for Eyebrow Image Segmentation

Traditional interactive image segmentation methods require users giving out background as well as foreground scribbles.Aiming at this problem,this paper proposes a novel image segmentation framework,named image segmentation with only positive and unlabeled examples.By combining learning from only positive and unlabeled examples method with graph-based semi-supervised learning technique,this method only needs users labeling a small number of pixels on interest region for segmentation.Experiments on the BJUT Eyebrow Database show that the proposed method achieves analogous results to graph-based semi-supervised learning,Random Walk as well as Lazy Snapping method,and is suitable for eyebrow recognition preprocessing.

Interactive image segmentation for ultrasound vascular imaging

Image segmentation for object to extract data from ultrasound acquired is an essential preprocessing step for the effective diagnosis. Various image segmentation methods have been studied. In this study, interactive image segmentation method by graph cut algorithm is proposed to develop a variety of applications of vascular ultrasound imaging and diagnostics. General imaging and vascular ultrasound imaging segmentation by entering constrain condition such as foreground and background. In the future it will be able to develop new ultrasound diagnostics.