Abstract: This paper details the design and implementation of ANGY, a rule-based expert system in the domain of medical image processing. Given a subtracted digital angiogram of the chest, ANGY identifies and isolates the coronary vessels, while ignoring any nonvessel structures which may have arisen from noise, variations in background contrast, imperfect subtraction, and irrelevent anatomical detail. The overall system is modularized into three stages: the preprocessing stage and the two stages embodied in the expert itself. In the preprocessing stage, low-level image processing routines written in C are used to create a segmented representation of the input image. These routines are applied sequentially. The expert system is rule-based and is written in OPS5 and LISP. It is separated into two stages: The low-level image processing stage embodies a domain-independent knowledge of segmentation, grouping, and shape analysis. Working with both edges and regions, it determines such relations as parallel and adjacent and attempts to refine the segmentation begun by the preprocessing. The high-level medical stage embodies a domain-dependent knowledge of cardiac anatomy and physiology. Applying this knowledge to the objects and relations determined in the preceding two stages, it identifies those objects which are vessels and eliminates all others.

Abstract: A new algorithm for the segmentation of textured images is developed by making use of Gibbs random fields. A hierarchical stochastic model is employed to represent textured images. At the higher level, the region formation process, describing different areas of the image, is modeled as a Gibbs random field, or equivalently as a Markov random field. At the lower level, the textures in different regions of the image are modeled also as Gibbs random fields. Based on this hierarchical model, the segmentation algorithm being proposed seeks to obtain the maximum a posteriori estimate of the region process using the textured image data. The maximization is carried out recursively by making use of a dynamic programming formulation. Computational concerns, however, necessitate the implementation of a suboptimal version of the algorithm that tries to maximize a pseudolikelihood over strips of the image. This is a non-trivial extension of a maximum a posteriori segmentation algorithm for noisy images modeled by Gibbs random fields [1]. The segmentation algorithm is applied on several textured images composed of 2, 3 region (texture) types and 2 or 4 level textures, with remarkable success. Numerous examples on the application of the segmentation algorithm are presented for textured images with region processes and textures generated according to a particular Gibbs distribution.

Abstract: The formation of segmented structures is a very important step during development of higher organisms. With the formation of somites in vertebrates or the segments in insects the primary anteroposterior pattern of the organisms is laid down. Segmentation is the result of the superposition of two pattern formation processes. One generates a periodic pattern, i.e. a repetition of homologous structures. It consists in vertebrates of somites and somitic clefts and in insects of segments and segment borders. Superimposed on this periodic pattern is a sequential pattern which makes the repetitive subunits different from each other. In recent years, we have proposed molecularly feasible models which are able to generate periodic and sequential structures precisely superimposed on each other (Meinhardt, 1982a,b). For insect development more detailed experimental and genetic data are available. For that reason the model of insect segmentation is more advanced. At the beginning of this paper a short overview of the model proposed for insect segmentation will be provided. I will show that, with some adaptations, the basic mechanism is also able to account for somite formation.

Abstract: A method of processing a word with the segmentation and recognition steps combined into an overall scheme. This is accomplished by a three step procedure. First, potential or trail segmentation points are derived. This is done in a manner so as to ensure that essentially all true segmentation points are present but also includes extra or not true segmentation points. Second, all combinations of the segments that could reasonably be a character are sent to a character recognizer to obtain ranked choices and corresponding scores. Finally, the recognition results are sorted and combined so that the character sequences having the best cummulative scores are obtained as the best word choices. For a particular word choice there is a corresponding character segmentation, simply the segment combinations that resulted in the chosen characters. With this recognition scheme the initial character segmentation is not final and need not be highly accurate, but is subject to a lesser constraint of containing the true segmentation points.

Abstract: Image processing methods (segmentation) are presented in connection with a modeling of image structure. An image is represented as a set of primitives, characterized by their type, abstraction level, and a list of attributes. Entities (regions for example) are then described as a subset of primitives obeying particular rules. Image segmentation methods are discussed, according to the associated image modeling level. Their potential efficacity is compared, when applied to cytologic image analysis.

Abstract: The aim of picture segmentation is the extraction of pertinent and stable areas. Pertinence is the agreement of the detected areas with a physical or semantical property of the object; stability is the robustness of the detection to slight transformations such as geometric or photometric distortions. In aerial picture segmentation, the pertinence of an area is often reduced to radiometric homogeneity and spatial connectivity. Unfortunately stability is seldom checked and the deduced segmentation is very sensitive to many parameters introduced by the programmer and thus it is not very reliable. We propose a solution to the stability problem. It will be presented in a theoretical way and then an example of an application is proposed. This method makes use of the well-known split-and-merge algorithm and we will first recall its principle and its main properties.

Abstract: An image segmentation process was derived from an image model that assumed that cell images represent objects having characteristic relationships, limited shape properties and definite local color features. These assumptions allowed the design of a region-growing process in which the color features were used to iteratively aggregate image points in alternation with a test of the convexity of the aggregate obtained. The combination of both local and global criteria allowed the self-adaptation of the algorithm to segmentation difficulties and led to a self-assessment of the adequacy of the final segmentation result. The quality of the segmentation was evaluated by visual control of the match between cell images and the corresponding segmentation masks proposed by the algorithm. A comparison between this region-growing process and the conventional gray-level thresholding is illustrated. A field test involving 700 bone marrow cells, randomly selected from May-Grunwald-Giemsa-stained smears, allowed the evaluation of the efficiency, effectiveness and confidence of the algorithm: 96% of the cells were evaluated as correctly segmented by the algorithm's self-assessment of adequacy, with a 98% confidence. The principles of the other major segmentation algorithms are also reviewed.

Abstract: An approximation algorithm for two-dimensional (2-D) signals, e.g. images, is presented. This approximation is obtained by partitioning the original signal into adjacent regions with each region being approximated in the least square sense by a 2-D analytical function. The segmentation procedure is controlled iteratively to insure at each step the best possible quality between the original image and the segmented one. The segmentation is based on two successive steps: splitting the original picture into adjacent squares of different size, then merging them in an optimal way into the final region configuration. Some results are presented when the approximation is performed by polynomial functions.

Abstract: There are several limitations of the recursive region splitting algorithm for image segmentation. The recursive region splitting at hierarchical scope view is a new algorithm to ease some of the difficulties. A quad tree structure is used to store the split results of the scope views at different levels. The segmentation will proceed to small scope views only if the result at that level is not satisfactory according to a certain criterion. Experimental results of applying this new algorithm to aerial photographs shows improved sensitivity to detect smaller objects. A boundary check procedure is implemented in this algorithm to remove boundary discontinuity along the scope view border. The segmentation results and processing time of four sets of aerial photographs are also discussed here.

Abstract: Computer vision systems applicable for the analysis of complex high resolution aerial images require reliable and robust operators for extracting information from the images. These operators should be able to interrogate the image data and derive meaningful information about the presence of various objects appearing in the scene. In this paper we describe the development of operators that are derived from a texture analysis methodology for performing segmentation of high resolution imagery. The utility of these operators for characterizing various perceptually meaningful properties is demonstrated by performing experimental analysis of urban scenes.

Abstract: Image segmentation algorithms based on hierarchical clustering have been developed for analysis of T 1 and T 2nuclear magnetic resonanceimages. Application of these algorithms to simultaneous T 1–T 2images of healthy volunteers extracted fundamental tissue types in the brain. These algorithms also were used both to identify the extent of the region of involvement of a subject with a history of a grade 3 astrocytoma of the right frontal lobe of the brain, and to characterize the tissue within the region of involvement. These results suggest that a simple segmentation algorithm can produce reasonable clustering of tissue types within the brain.

Abstract: Experiments are described which indicate that the integration of high-precision shape information along a bright line is blocked by the presence of certain image features. All the features involved have three properties: (1) they are points where contours are not smooth (i.e. not twice differentiable) within the limits set by the finite space constants of visual processes; (2) they are all points that are emphasized in the responses of certain classes of circularly symmetric bandpass spatial filter; and (3) they are all significant for three-dimensional shape analysis. The results are interpreted as implying an inflexible segmentation of the contour image before detailed shape analysis.

Abstract: : A possible approach to image segmentation is first to perform a low- level segmentation. This then allows an original image to be described in terms of a set of simple regions or primitives. Objects in the image may be subsequently recognized by matching these primitives to patterns of primitives in a data base. It is found that current techniques for low-level image segmentation fail when applied to high noise images. An algorithm is presented which overcomes the problems associated with high noise and succeeds in generating low-level segmentations of noisy imagery. The algorithm is shown also to work on low noise data.

Abstract: A pyramid linking algorithm for texture segmentation is presented. It is based on the computation of spatial properties of long, straight edge segments at fixed orientations. Features are computed for each edge segment in terms of the distances to the nearest neighboring edge segments of given orientations. This produces a set of sparse “edge separation maps” of features which are then used as the basis of a pyramid linking procedure for hierarchically grouping edges into homogeneously textured regions. Segmentation is performed in one bottom-up pass of linking nodes to their most similar parent. Results are shown using both the raw and smoothed edge separation features. All of the steps of the procedure can be efficiently implemented as parallel operations on a pyramid machine.

Abstract: We present a new segmentation algorithm based upon a region growing technique. An initial segmentation is obtained using a MERGE procedure described by Pavlidis. We then build an adjacency graph of regions pairs.

Abstract: Machine extraction of meaningful features from the digitized representation of an image (picture, scene etc.) is of great interest to investigators working in such diverse fields as robotic vision, scene analysis, pattern recognition, and automatic part identification in manufacturing processes. The authors describe in detail their algorithms for implementing different segmentation strategies. These are a label propagation segmentation scheme (using the region growing algorithm) and a linked pyramid segmentation scheme. The two techniques are analyzed and compared with respect to their ability to satisfactorily segment a wide class of images (scenes, radiographs, machine parts etc.); computational overheads; memory overheads; and sensitivity to additive noise (Gaussian). In addition to the critical analysis and evaluation of the two techniques, the authors introduce the following enhancements: new predicates (similarity criteria) that are applicable to a broad class of images; incorporation of impulse noise suppression; hierarchical two-level processing to refine segmentation by label propagation; and use of a weighting function to improve the segmentation process.

Abstract: In this paper we describe an adaptation of our two source RBC algorithm[1] where we introduce a variable block size. The blocks are chosen to meet some uniformity criterion and are generated via a quad-tree segmentation of the image which allows the segmentation overhead to be coded very efficiently. The prediction component is improved over standard RBC prediction with a fixed block size and some images may be coded using the prediction alone. However it is more economical to also code the residual component for more detailed images.

Abstract: A parallel algorithm for syntactic image segmentation is introduced. Stochastic tree grammar is used as a context-generating model. It is shown that when this context-generating process is in the equilibrium state, a matched filter can be designed and applied in parallel to the image. This process can be used for image segmentation in a syntactic pattern recognition system to enhance the performance of the succeeding recognition process.

Abstract: In robotics, pattern recognition systems often have to take decisions and provide results to higher level systems. They can do this simply by refering to a dictionary of patterns in a well defined and limited universe. This paper describes a multiprocessor system which makes in real time a matching by using dynamic programming between a segmentation of reference and an unknown segmentation, in order to provide a likeness coefficient. Application in pattern recognition (speech, vision) but also in decision (dynamic programming in local area ie edge detection) or even in general parallel computer.

Abstract: Recently, ways to obtain a new generation of image-coding techniques have been proposed. The incorpordtion of the human visual system (IIVS) models and tools of the image analysis, such as segmentation, are two defining features of these techniques. In this paper, an application of the new approach to the classical linear predictive coding (LPC) of images and an HVS based segmentation technique for the second genera-tion coders will be discussed. In the case of LPC, the error image is encoded using an image decomposition approach and binary image coding. This improves the compression ratio keeping the quality nearly the same. The new segmentation technique can be used in single frame image coding applications to obtain acceptable images at extremely high compression rates.