Showing papers on "Segmentation published in 1995"
TL;DR: A 3‐D model‐based segmentation method is presented in this paper for the completely automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI).
Abstract: Explicit segmentation is required for many forms of quantitative neuroanatomic analysis However, manual methods are time-consuming and subject to errors in both accuracy and reproducibility (precision) A 3-D model-based segmentation method is presented in this paper for the completely automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI)
The approach depends on a general, iterative, hierarchical non-linear registration procedure and a 3-D digital model of human brain anatomy that contains both volumetric intensity-based data and a geometric atlas Here, the traditional segmentation strategy is inverted: instead of matching geometric contours from and idealized atlas directly to the MRI data, segmentation is achieved by identifying the non-linear spatial transformation that best maps corresponding intensity-based features between a model image and a new MRI brain volume When completed, atlas contours defined on the model image are mapped through the same transformation to segment and label individual structures in the new data set
Using manually segmented sturcture boundaries for comparison, measures of volumetric difference and volumetric overlap were less than 2% and better than 97% for realistic brain phantom data, and less than 10% and better than 85%, respectively, for human MRI data This compares favorably to intra-observer variability estimates of 49% and 87%, respectively The procedure performs well, is objective and its implementation robust The procedure requires no manual intervention, and is thus applicable to studies of large numbers of subjects The general method for non-linear image matching is also useful for non-linear mapping of brain data sets into stereotaxic space if the target volume is already in stereotaxic space © 1995 Wiley-Liss, Inc
1,088 citations
TL;DR: The information provided by the user's selected points is explored and an optimal method to detect contours which allows a segmentation of the image is applied, based on dynamic programming (DP), and applies to a wide variety of shapes.
Abstract: The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images. >
532 citations
TL;DR: A physics-based approach to anatomical surface segmentation, reconstruction, and tracking in multidimensional medical images using a dynamic "balloon" model--a spherical thin-plate under tension surface spline which deforms elastically to fit the image data.
453 citations
20 Jun 1995
TL;DR: The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing the response to edges, leading to an efficient, parameter-free implementation.
Abstract: Presents a novel, parameter-free technique for the segmentation and local description of line structures on multiple scales, both in 2D and in 3D. The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing the response to edges. The filtering process creates one sharp maximum across the line-feature profile and across the scale-space. The multi-scale response reflects local contrast and is independent of the local width. The filter is steerable in both the orientation and scale domains, leading to an efficient, parameter-free implementation. A local description is obtained that describes the contrast, the position of the center-line, the width, the polarity, and the orientation of the line. Examples of images from different application domains demonstrate the generic nature of the line segmentation scheme. The 3D filtering is applied to magnetic resonance volume data in order to segment cerebral blood vessels. >
388 citations
TL;DR: The feature-based optic flow field is segmented into clusters with affine internal motion which are tracked over time and runs in real-time, and is accurate and reliable.
Abstract: This paper describes a system for detecting and tracking moving objects in a moving world. The feature-based optic flow field is segmented into clusters with affine internal motion which are tracked over time. The system runs in real-time, and is accurate and reliable. >
326 citations
TL;DR: This work proposes a new algorithm of range data registration and segmentation that is robust in the presence of outlying points (outliers) like noise and occlusion and integrates the inliers obtained from multiple range images to construct a data set representing an entire object.
315 citations
TL;DR: A new neuro-cognitive Visual Attention Model, called VAM, is a model of visual attention control of segmentation, object recognition, and space-based motor action that solves the “inter- and intra-object-binding problem”.
Abstract: This paper introduces a new neuro-cognitive Visual Attention Model, called VAM. It is a model of visual attention control of segmentation, object recognition, and space-based motor action. VAM is concerned with two main functions of visual attention-that is “selection-for-object-recognition” and “selection-for-space-based-motor-action”. The attentional control processes that perform these two functions restructure the results of stimulus-driven and local perceptual grouping and segregation processes, the “visual chunks”, in such a way that one visual chunk is globally segmented and implemented as an “object token”. This attentional segmentation solves the “inter- and intra-object-binding problem”. It can be controlled by higher-level visual modules of the what-pathway (e.g. V4/IT) and/or the where-pathway (e.g. PPC) that contain relatively invariant “type-level” information (e.g. an alphabet of shape primitives, colors with constancy, locations for space-based motor actions). What-based attention...
253 citations
TL;DR: The approach proposed here is inspired and influenced by well established video production processes and is used to classify the transition effects used in video and to design automatic edit effect detection algorithms.
Abstract: Effective and efficient tools for segmenting and content-based indexing of digital video are essential to allow easy access to video-based information. Most existing segmentation techniques do not use explicit models of video. The approach proposed here is inspired and influenced by well established video production processes. Computational models of these processes are developed. The video models are used to classify the transition effects used in video and to design automatic edit effect detection algorithms. Video segmentation has been formulated as a production model based classification problem. The video models are also used to define segmentation error measures. Experimental results from applying the proposed technique to commercial cable television programming are presented.
228 citations
1 Jan 1995
TL;DR: In this paper, a brain tissue probability model was used for segmentation of multiple sclerosis (MS) lesions in magnetic resonance (MR) brain images, and an empirical comparison of the performance of statistical and decision tree classifiers, applied to MS lesion segmentation.
Abstract: Human investigators instinctively segment medical images into their anatomical components, drawing upon prior knowledge of anatomy to overcome image artifacts, noise, and lack of tissue contrast. This paper describes: 1) the development and use of a brain tissue probability model for the segmentation of multiple sclerosis (MS) lesions in magnetic resonance (MR) brain images, and 2) an empirical comparison of the performance of statistical and decision tree classifiers, applied to MS lesion segmentation. Based on MR image data obtained from healthy volunteers, the model provides prior probabilities of brain tissue distribution per unit voxel in a standardized 3-D "brain space." In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of MS lesions reduced the volume of false positive lesions by 5040%.
227 citations
Book•
1 Jan 1995
TL;DR: This paper presents a simple method for document image segmentation in which text regions in a given document image are automatically identified and is shown to work even for skewed images and handwritten text.
Abstract: There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique.
206 citations
3 Apr 1995
TL;DR: A unifying definition and a classification scheme for existing VB matching criteria and a new matching criterion: the entropy of the grey-level scatter-plot, which requires no segmentation or feature extraction and no a priori knowledge of photometric model parameters.
Abstract: In this paper, 3D voxel-similarity-based (VB) registration algorithms that optimize a feature-space clustering measure are proposed to combine the segmentation and registration process. We present a unifying definition and a classification scheme for existing VB matching criteria and propose a new matching criterion: the entropy of the grey-level scatter-plot. This criterion requires no segmentation or feature extraction and no a priori knowledge of photometric model parameters. The effects of practical implementation issues concerning grey-level resampling, scatter-plot binning, parzen-windowing and resampling frequencies are discussed in detail and evaluated using real world data (CT and MRI).
1 Dec 1995
TL;DR: Algorithms for automatic character segmentation in motion pictures which extract automatically and reliably the text in pre-title sequences, credit titles, and closing sequences with title and credits are developed.
Abstract: We have developed algorithms for automatic character segmentation in motion pictures which extract automatically and reliably the text in pre-title sequences, credit titles, and closing sequences with title and credits. The algorithms we propose make use of typical characteristics of text in videos in order to enhance segmentation and, consequently, recognition performance. As a result, we get segmented characters from video pictures. These can be parsed by any OCR software. The recognition results of multiple instances of the same character throughout subsequent frames are combined to enhance recognition result and to compute the final output. We have tested our segmentation algorithms in a series of experiments with video clips recorded from television and achieved good segmentation results.
TL;DR: A new paradigm for the segmentation of range images into piecewise continuous surfaces is presented through an effective combination of simple component algorithms, which stands in contrast to methods which attempt to solve the problem in a single processing step using sophisticated means.
Abstract: Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. In this paper we present a new paradigm for the segmentation of range images into piecewise continuous surfaces. Data aggregation is performed via model recovery in terms of variable-order bi-variate polynomials using iterative regression. Model recovery is initiated independently in regularly placed seed regions in the image. All the recovered models are potential candidates for the final description of the data. Selection of the models is defined as a quadratic Boolean problem, and the solution is sought by the WTA (winner-takes-all) technique, which turns out to be a good compromise between the speed of computation and the accuracy of the solution. The overall efficiency of the method is achieved by combining model recovery and model selection in an iterative way. Partial recovery of the models is followed by the selection (optimization) procedure and only the “best” models are allowed to develop further. The major novelty of the approach lies in an effective combination of simple component algorithms, which stands in contrast to methods which attempt to solve the problem in a single processing step using sophisticated means. We present the results on several real range images.
TL;DR: A multilayer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks, which can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved.
Abstract: We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than using an edge-based approach to represent the segmentation of a scene, we propose a multilayer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks. This support-based approach can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved. Our framework is based on an extension of robust estimation methods that provide a theoretical basis for support-based estimation. We use a selection criteria derived from the minimum description length principle to decide how many support maps to use in describing an image. Our method has been applied to a number of different domains, including the decomposition of range images into constituent objects, the segmentation of image sequences into homogeneous higher-order motion fields, and the separation of tracked motion features into distinct rigid-body motions. >
3 Apr 1995
TL;DR: A 2D model is applied to segment structures from medical images with complex shapes and topologies, such as arterial “trees”, that cannot easily be segmented with traditional deformable models.
Abstract: This paper presents a technique for the segmentation of anatomic structures in medical images using a topologically adaptable snakes model. The model is set in the framework of domain subdivision using simplicial decomposition. This framework allows the model to maintain all of the strengths associated with traditional snakes while overcoming many of their limitations. The model can flow into complex shapes, even shapes with significant protrusions or branches, and topological changes are easily sensed and handled. Multiple instances of the model can be dynamically created, can seamlessly split or merge, or can simply and quickly detect and avoid collisions. Finally, the model can be easily and dynamically converted to and from the traditional parametric snakes model representation. We apply a 2D model to segment structures from medical images with complex shapes and topologies, such as arterial “trees”, that cannot easily be segmented with traditional deformable models.
20 Jun 1995
TL;DR: The paper describes how image sequences taken by a moving video camera may be processed to detect and track moving objects against a moving background in real-time.
Abstract: The paper describes how image sequences taken by a moving video camera may be processed to detect and track moving objects against a moving background in real-time. The motion segmentation and shape tracking system as known as ASSET-2-A Scene Segmenter Establishing Tracking, Version 2. Motion is found by tracking image features, and segmentation is based on first-order (i.e., six parameter) flow fields. Shape tracking is performed using two dimensional radial map representation. The system runs in real-time, and is accurate and reliable. It requires no camera calibration and no knowledge of the camera's motion. >
TL;DR: The authors report the results of an extensive testing program aimed at investigating the behavior of important experimental parameters such as the probability of correct classification and the accuracy of curvature estimates, measured over variations of significant segmentation variables.
Abstract: This paper focuses on the experimental evaluation of a range image segmentation system which partitions range data into homogeneous surface patches using estimates of the sign of the mean and Gaussian curvatures. The authors report the results of an extensive testing program aimed at investigating the behavior of important experimental parameters such as the probability of correct classification and the accuracy of curvature estimates, measured over variations of significant segmentation variables. Evaluation methods in computer vision are often unstructured and subjective: this paper contributes a useful example of extensive experimental assessment of surface-based range segmentation. >
TL;DR: Single-image segmentation methods seem to have high sensitivity in selecting true-positive mass regions in the first stage of a CAD scheme and a multilayer topographic image feature analysis method in the second stage has the potential to significantly reduce the false-positive detection rate.
TL;DR: To find the ideal segmentation, the authors develop a stopping criterion for their Iterative Parallel Region Growing (IPRG) algorithm using additional information from edge features, and the Hausdorff distance metric.
Abstract: A basic requirement for understanding the dynamics of the Earth's major ecosystems is accurate quantitative information about the distribution and areal extent of the Earth's vegetation formations. Some of this required information can be obtained through the analysis of remotely sensed data. Image segmentation is often one of the first steps of this analysis. This paper focuses on two particular types of segmentation: region-based and edge-based segmentations. Each approach is affected differently by various factors, and both types of segmentations may be improved by taking advantage of their complementary nature. Included among region-based segmentation approaches are region growing methods, which produce hierarchical segmentations of images from finer to coarser resolution. In this hierarchy, an ideal segmentation (ideal for a given application) does not always correspond to one single iteration, but map correspond to several different iterations. This, among other factors, makes it somewhat difficult to choose a stopping criterion for region growing methods. To find the ideal segmentation, the authors develop a stopping criterion for their Iterative Parallel Region Growing (IPRG) algorithm using additional information from edge features, and the Hausdorff distance metric. They integrate information from regions and edges at the symbol level, taking advantage of the hierarchical structure of the region segmentation results. Also, to demonstrate the feasibility of this approach in processing the massive amount of data that will be generated by future Earth remote sensing missions, such as the Earth Observing System (EOS), all the different steps of this algorithm have been implemented on a massively parallel processor. >
TL;DR: The authors explore the potential of artificial neural networks in assisting industrial marketers faced with a segmentation problem by comparing their classification ability with discriminant analysis and logistic regression.
23 Oct 1995
TL;DR: This paper presents efficient algorithms for determining the language classification of machine generated documents without requiring the identification of individual characters using the less computationally intensive methods described.
Abstract: This paper presents efficient algorithms for determining the language classification of machine generated documents without requiring the identification of individual characters. Such algorithms may be useful for sorting and routing of facsimile documents as they arrive so that appropriate routing and secondary analysis, which may include OCR, is selected for each document. It may also prove useful as a component of a content addressable document access system. There have been numerous reported efforts which attempt to segment printed documents into homogeneous regions using Hough transforms, hidden Markov models, morphological filtering, and neural networks. However, language identification can be accomplished without explicit segmentation using the less computationally intensive methods described.
TL;DR: An automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs) that compares favorably with respect to other successful schemes reported in the literature.
Abstract: In this paper, we describe an automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs). First, the feature map of the image is formed using Laws' micromasks and directional macromasks. Each pixel in the feature map is represented by a sequence of 4-D feature vectors. The feature sequences belonging to the same texture are modeled as an HMM. Thus, if there are M different textures present in an image, there are M distinct HMMs to be found and trained. Consequently, the unsupervised texture segmentation problem becomes an HMM-based problem, where the appropriate number of HMMs, the associated model parameters, and the discrimination among the HMMs become the foci of our scheme. A two-stage segmentation procedure is used. First, coarse segmentation is used to obtain the approximate number of HMMs and their associated model parameters. Then, fine segmentation is used to accurately estimate the number of HMMs and the model parameters. In these two stages, the critical task of merging the similar HMMs is accomplished by comparing the discrimination information (DI) between the two HMMs against a threshold computed from the distribution of all DI's. A postprocessing stage of multiscale majority filtering is used to further enhance the segmented result. The proposed scheme is highly suitable for pipeline/parallel implementation. Detailed experimental results are reported. These results indicate that the present scheme compares favorably with respect to other successful schemes reported in the literature. >
23 Oct 1995
TL;DR: This paper proposes a new technique which sequentially refines the segmentation and the motion estimation by combining static segmentations and motion information.
Abstract: The problem of segmenting an image sequence in terms of regions characterized by a coherent motion is among the most challenging in image sequence analysis. This paper proposes a new technique which sequentially refines the segmentation and the motion estimation by combining static segmentation and motion information. The motion is robustly computed by a global estimation which remove the camera motion, followed by a local estimation using a matching technique and a robust estimator. Simulation results show the efficiency of the proposed technique.
TL;DR: This technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics to reduce computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm.
14 Aug 1995
TL;DR: In this paper, a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images is proposed.
Abstract: Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touching or overlapping characters may be lost. If we analyze gray-scale images, however, specific topographic features and the variation of intensity can be observed in the character boundaries. We believe that such kinds of clues obtained from gray-scale images should be useful for efficient character segmentation. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted form gray-scale images. Then the nonlinear character segmentation path in each character segmentation region is found by using multistage graph search algorithm. Finally, in order to confirm the character segmentation paths and recognition results, recognition based segmentation method is adopted.
Journal Article•
23 Oct 1995
TL;DR: The authors present a method that combines region growing and edge detection for magnetic resonance (MR) brain image segmentation by applying a sophisticated region merging method which is capable of handling complex image structures.
Abstract: The authors present a method that combines region growing and edge detection for magnetic resonance (MR) brain image segmentation. Starting with a simple region growing algorithm which produces an over segmented image, the authors apply a sophisticated region merging method which is capable of handling complex image structures. Edge information is then integrated to verify and, where necessary, to correct region boundaries. The results show that this method is reliable and efficient for MR brain image segmentation.
TL;DR: A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented and has been tested on over hundred images from several patient studies.
TL;DR: The MLSOFM combines the ideas of self-organization and topographic mapping with those of multiscale image segmentation, and is formulated as one of vector quantization and is mapped onto the MLSSOFM.
TL;DR: This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition using Daubechies four-tap filter that propagates through the pyramid to a higher resolution with continuously improving the segmentation.