Showing papers on "Image segmentation published in 1992"

Boundary finding with parametrically deformable models

Abstract: Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based global shape information. The authors apply flexible constraints, in the form of a probabilistic deformable model, to the problem of segmenting natural 2-D objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form. The parametric model is based on the elliptic Fourier decomposition of the boundary. Probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations. Boundary finding is formulated as an optimization problem using a maximum a posteriori objective function. Results of the method applied to real and synthetic images are presented, including an evaluation of the dependence of the method on prior information and image quality. >

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Abstract: Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared. >

An adaptive clustering algorithm for image segmentation

Abstract: The problem of segmenting images of objects with smooth surfaces is considered. The algorithm that is presented is a generalization of the K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in an iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an 8-neighbor Gibbs random field model applied to pictures of industrial objects, buildings, aerial photographs, optical characters, and faces show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions that incorporate spatial constraints by the use of Gibbs random fields. A hierarchical implementation is also presented that results in better performance and faster speed of execution. The segmented images are caricatures of the originals which preserve the most significant features, while removing unimportant details. They can be used in image recognition and as crude representations of the image. >

The Watershed Transformation Applied to Image Segmentation

Abstract: Image segmentation by mathematical morphology is a methodology based upon the notions of watershed and homotopy modification. This paper aims at introducing this methodology through various examples of segmentation in materials sciences, electron microscopy and scene analysis. First, we define our basic tool, the watershed transform. We show that this transformation can be built by implementing a flooding process on a grey-tone image. This flooding process can be performed by using elementary morphological operations such as geodesic skeleton and reconstruction. Other algorithms are also briefly presented (arrows representation). Then, the use of this transformation for image segmentation purposes is discussed. The application of the watershed transform to gradient images and the problems raised by over-segmentation are emphasized. This leads, in the third part, to the introduction of a general methodology for segmentation, based on the definition of markers and on a transformation called homotopy modification. This complex tool is defined in detail and various types of implementation are given. Many examples of segmentation are presented. These examples are taken from various fields : transmission electron microscopy, SEM, 3D holographic pictures, radiography, non destructive control and so on. The final part of this paper is devoted to the use of the watershed transformation for hierarchical segmentation. This tool is particularly efficient for defining different levels of segmentation starting from a graph representation of the images based on the mosaic image transform. This approach will be explained by means of examples in industrial vision and scene analysis.

Text segmentation using Gabor filters for automatic document processing

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.

Computer Vision and Image Processing

Abstract: On the use of morphological operators in a class of edge detectors, L. Hertz and R.W. Schafer a valley-seeking threshold selection technique, S.C. Sahasrabudhe and K.S. Das Gupta local characteristics of binary images and their application to the automatic control of low-level robot vision, P.W. Pachowicz corner detection and localization in a pyramid, S. Baugher and A. Rosenfeld parallel-hierarchical image partitioning and region extraction, G.N. Khan and D.F. Gillies invariant architectures for low-level vision, L. Jacobson and H. Wechsler representation - primitives chain code, L. O'Gorman generalized cones - useful geometric properties, K. Rao and G. Medioni vision-based rendering - image synthesis for vision feature algorithms, J.D. Yates, et al recognition - investigation of a number of character recognition algorithms, A.A. Verikas, et al log-polar mapping applied to pattern representation and recognition, J.C. Wilson and R.M. Hodgson pattern recognition of binary image objects using morphological shape decomposition, I. Pitas and N.D. Sidiropoulos a pattern classification approach to multi-level thresholding for image segmentation, J.G. Postaire and M. Ameziane KOR - a knowledge-based object recognition system, C.M. Lee, et al shape decomposition based on perceptual structure, H.S. Kim and K.H. Park three dimensional - the Frobenius metric in image registration, K. Zikan and T.M. Silberberg binocular fusion revisited utilizing a log-polar tessellation, N.C. Griswold, et al an expert system for recovering 3D shape and orientation from a single view, W.J. Shomar, et al integrating intensity and range sensing to construct 3D polyhedra representation, W.N. Lie, et al notes - texture segmentation using topographic labels, T.C. Pong, et al an improved algorithm for labelling connected components in a binary image, X.D. Yang a note on the paper "The Visual Potential - One Convex Polygon", A. Laurentini a string descriptor for matching partial shapes, H.C. Liu and M.D. Srinath formulation and error analysis for a generalized image point correspondence algorithm, S. Fotedar, et al a new surface tracking system in 3D binary images, L.W. Chang and M.J. Tsai.

Interactive 3D segmentation of MRI and CT volumes using morphological operations.

Abstract: Analysis of tomographic volume imagery would be greatly facilitated if the objects within the volume could be presented in three-dimensionally (3D) rendered views. Such a capability has not been developed in large part because the problem of image segmentation remains unsolved. We describe a

Image compression with variable block size segmentation

Abstract: High-quality variable-rate image compression is achieved by segmenting an image into regions of different sizes, classifying each region into one of several perceptually distinct categories, and using a distinct coding procedure for each category Segmentation is performed with a quadtree data structure by isolating the perceptually more important areas of the image into small regions and separately identifying larger random texture blocks Since the important regions have been isolated, the remaining parts of the image can be coded at a lower rate than would be otherwise possible High-quality coding results are achieved at rates between 035 and 07 b/p depending on the nature of the original image, and satisfactory results have been obtained at 025 b/p >

Segmentation methods for character recognition: from segmentation to document structure analysis

Abstract: A pattern-oriented segmentation method for optical character recognition that leads to document structure analysis is presented. As a first example, segmentation of handwritten numerals that touch are treated. Connected pattern components are extracted, and spatial interrelations between components are measured and grouped into meaningful character patterns. Stroke shapes are analyzed and a method of finding the touching positions that separates about 95% of connected numerals correctly is described. Ambiguities are handled by multiple hypotheses and verification by recognition. An extended form of pattern-oriented segmentation, tabular form recognition, is considered. Images of tabular forms are analyzed, and frames in the tabular structure are extracted. By identifying semantic relationships between label frames and data frames, information on the form can be properly recognized. >

New approach to 3-D registration of multimodality medical images by surface matching

Abstract: Multimodality images obtained from medical imaging systems such as computed tomography (CT), magnetic resonance (MR) imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT), generally provide complementary characteristic and diagnostic information. Synthesis of these image data sets into a single composite image containing these complementary attributes in accurate registration and congruence would provide truly synergistic information about the object(s) under examination. We have developed a new method which produces such correlation using parametric Chamfer matching. The method is fast, accurate, and reproducible. Surfaces ar initially extracted from two different images to be matched using semi-automatic segmentation techniques. These surfaces are represented as contours with common features to be matched. A distance transformation is performed for one surface image, and a cost function for the matching process is developed using the distance image. The geometric transformation includes three- dimensional translation, rotation, and scaling to accommodate images of different position, orientation, and size. The matching process involves searching this multi-parameter space to find the best fit which minimizes the cost function. The local minima problem is addressed by using a large number of starting points. A pyramid multi-resolution approach is employed to speed up both the distance transformation and the multi-parameter minimization processes. Robustness in noise handling is accomplished using multiple thresholds embedded in the multi- resolution search. The algorithm can register both partially overlapped and fragmented surfaces. Manual intervention is generally not necessary. Preliminary results suggest registration accuracy on the order of the voxel size used in the registration process. Computational time scales with the number of matching elements used, with about five minutes typical for 2563 images using a modern desktop workstation.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Color image segmentation

Abstract: Segmentation is a key problems in image processing. In the framework of mathematical morphology the traditional method of segmentation is based on the watershed transform. This method may be analysed as a region growing algorithm, starting from a set of markers for all zones of interest. The author extends it to the segmentation of color images and illustrates it, with paintings.< >

Perceptual organization for scene segmentation and description

Abstract: A data-driven system for segmenting scenes into objects and their components is presented. This segmentation system generates hierarchies of features that correspond to structural elements such as boundaries and surfaces of objects. The technique is based on perceptual organization, implemented as a mechanism for exploiting geometrical regularities in the shapes of objects as projected on images. Edges are recursively grouped on geometrical relationships into a description hierarchy ranging from edges to the visible surfaces of objects. These edge groupings, which are termed collated features, are abstract descriptors encoding structural information. The geometrical relationships employed are quasi-invariant over 2-D projections and are common to structures of most objects. Thus, collations have a high likelihood of corresponding to parts of objects. Collations serve as intermediate and high-level features for various visual processes. Applications of collations to stereo correspondence, object-level segmentation, and shape description are illustrated. >

Neural correlates of perceptual motion coherence

Abstract: The motions of overlapping contours in a visual scene may arise from the physical motion(s) of either a single or multiple surface(s). A central problem facing the visual motion system is that of assigning the most likely interpretation. The rules underlying this perceptual decision can be explored using a visual stimulus formed by superimposing two moving gratings. The resultant percept is either that of a single coherently moving 'plaid pattern' (coherent motion) or of the two component gratings sliding noncoherently across one another (noncoherent motion). When plaid patterns are configured to mimic one transparent grating overlying another, the percept of noncoherent motion dominates. We now report that neurons in the visual cortex of rhesus monkeys exhibit changes in direction tuning that parallel this perceptual phenomenon: sensitivity to the motions of the component gratings is enhanced under conditions that favour the perception of noncoherent motion. These results challenge models of cortical visual processing that fail to take into account the contribution of figural image segmentation cues to the analysis of visual motion.

Nested-hierarchical scene models and image segmentation

Abstract: An improved model of scenes for image analysis purposes, a nested-hierarchical approach which explicitly acknowledges multiple scales of objects or categories of objects, is presented. A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modeled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while interregion distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions. Application of the segmentation algorithm for forest stand delineation in TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely stocked forest stands which have high internal variance.

Segmenting images using normalized color

Abstract: An algorithm for segmenting images of 3-D scenes is presented. From an input color image, the algorithm determines the number of materials in the scene and labels each pixel according to the corresponding material. This segmentation is useful for many visual tasks including 3-D inspection and 3-D object recognition. The segmentation algorithm is based on a detailed analysis of the physics underlying color image formation and may be applied to images of a wide range of materials and surface textures. An initial edge detection on the intensity image is used to guide the segmentation process and to ensure accurate localization of region boundaries. The algorithm is based on the computation of local image features and can be mapped effectively onto high-performance parallel hardware. Issues related to illumination and sensors are addressed. Experimental results obtained for several images are presented. >

Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing

Abstract: A method for parameter estimation in image classification or segmentation is studied within the statistical frame of finite mixture distributions. The method models an image as a finite mixture. Each mixture component corresponds to an image class. Each image class is characterized by parameters, such as the intensity mean, the standard deviation, and the number of image pixels in that class. The method uses a maximum likelihood (ML) approach to estimate the parameters of each class and employs information criteria of Akaike (AIC) and/or Schwarz and Rissanen (MDL) to determine the number of classes in the image. In computing the ML solution of the mixture, the method adopts the expectation maximization (EM) algorithm. The initial estimation and convergence of the ML-EM algorithm were studied. The accuracy in determining the number of image classes using AIC and MDL is compared. The MDL criterion performed better than the AIC criterion. A modified MDL showed further improvement. >

A fast segmentation scheme for white blood cell images

Abstract: Presents a fast segmentation scheme for automatic differential counting of white blood cells. The segmentation procedure consists of three phases. First a novel simple algorithm is proposed for localization of white blood cells. The algorithm is based on a priori information about blood smear images. In the second phase the different cell components are separated with automatic thresholding. The thresholds are selected with a simple recursive method derived from maximizing the interclass variance between dark, gray and bright regions based on the method proposed by Otsu (1979). Finally the segmented regions are smoothed by morphological operations. The segmentation scheme works successfully for classification of white blood cells. Some experimental results are also presented. >

Segmentation of polarimetric synthetic aperture radar data

Abstract: A statistical image model is proposed for segmenting polarimetric synthetic aperture radar (SAR) data into regions of homogeneous and similar polarimetric backscatter characteristics. A model for the conditional distribution of the polarimetric complex data is combined with a Markov random field representation for the distribution of the region labels to obtain the posterior distribution. Optimal region labeling of the data is then defined as maximizing the posterior distribution of the region labels given the polarimetric SAR complex data (maximum a posteriori (MAP) estimate). Two procedures for selecting the characteristics of the regions are then discussed. Results using real multilook polarimetric SAR complex data are given to illustrate the potential of the two selection procedures and evaluate the performance of the MAP segmentation technique. It is also shown that dual polarization SAR data can yield segmentation resultS similar to those obtained with fully polarimetric SAR data. >

Anatomy of a color histogram

Abstract: It is shown that the color histogram has an even closer relationship to scene properties than has been previously described. Color histograms have identifiable features that relate in a precise mathematical way to scene properties. Object color and illumination color are the most obvious properties that are related to color distribution, and their extraction has already been described. It is shown here that the histogram of color variation may be further exploited to relate its shape to surface roughness and imaging geometry. An understanding of these features allows an improved estimate of illumination color and object color to be made. >

Image registration of multimodality 3-D medical images by chamfer matching

Abstract: Images from computed tomography (CT), magnetic resonance (MR) imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT), etc., provide complementary characteristic and diagnostic information. A parametric Chamfer Matching method is used for fast and accurate registration of the images from different medical imaging modalities. Surfaces are initially extracted from two images to be matched using semi-automatic segmentation software, and then these surfaces are used as common features to be matched. A distance transformation is performed for one surface image, and an error function is developed using the distance-image to evaluate the matching error. The geometric transformation includes three-dimensional translation, rotation, and scaling parameters to accommodate images of different position, orientation, and size. The matching process involves searching the multi-parameter space to find the fit which will minimize the error function. The local minima problem is addressed by using a large number of starting points. A pyramid multiresolution approach is employed to speed up both the distance transformation and the multi-parameter minimization processes. Robustness in handling noise is enhanced by using multiple thresholds approach imbedded in the multi-resolution process. Human intervention is not necessary.

Color segmentation using perceptual attributes

Abstract: A general approach for achieving color image segmentation using uniform-chromaticity-scale perceptual color attributes is proposed. At first chromatic and achromatic areas in a perceptual IHS color space are defined. Then the image is separated into chromatic and achromatic regions according to the region locations in the color space. 1-D histogram thresholding for each color attribute is performed to split the chromatic and achromatic regions, respectively. Finally the region growing is used to solve the oversegmentation problem. In an experiment the power of the proposed approach is demonstrated. >

Model-based segmentation of individual brain structures from MRI data

Abstract: This paper proposes a methodology that enables an arbitrary 3-D MRI brain image-volume to be automatically segmented and classified into neuro-anatomical components using multiresolution registration and matching with a novel volumetric brain structure model (VBSM). This model contains both raster and geometric data. The raster component comprises the mean MRI volume after a set of individual volumes of normal volunteers have been transformed to a standardized brain-based coordinate space. The geometric data consists of polyhedral objects representing anatomically important structures such as cortical gyri and deep gray matter nuclei. The method consists of iteratively registering the data set to be segmented to the VBSM using deformations based on local image correlation. This segmentation process is performed hierarchically in scale-space. Each step in decreasing levels of scale refines the fit of the previous step and provides input to the next. Results from phantom and real MR data are presented.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Fractal dimension in the analysis of medical images

Abstract: The analysis of cardiac magnetic resonance (MR) images and X-rays of bone is considered. Each image type is approached using a different form of fractal parameterization. For the MR images, the goal of the study is segmentation, and to this end small regions of the image are assigned a local value of fractal dimension. For the bone X-rays, rather than segmentation, the large-scale structure is parameterized by its fractal dimension. In both cases, the use of fractals leads to the classification of the parameters of interest. When applied to segmentation, this analysis yields boundary discrimination unavailable through previous methods. For the X-rays, texture changes are quantified and correlated with physical changes in the subject. In both cases, the parameterizations are robust with regard to noise present in the images, as well as to variable contrast and brightness. >

Combining Intensity and Motion for Incremental Segmentation and Tracking Over Long Image Sequences

Abstract: This paper presents a method for incrementally segmenting images over time using both intensity and motion information. This is done by formulating a model of physically significant image resgions using local constraints on intensity and motion and then finding the optimal segmentation over time using an incremental stochastic minimization technique. The result is a robust and dynamic segmentation of the scene over a sequence of images. The approach has a number of benefits. First, discontinuities are extracted and tracked simultaneously. Second, a segmentation is always available and it improves over time. Finally, by combining motion and intensity, the structural properties of discontinuities can be recovered; that is, discontinuities can be classified as surface markings or actual surface boundaries.