Showing papers on "Range segmentation published in 2006"

Supervised learning-based cell image segmentation for P53 immunohistochemistry

Abstract: In this paper, we present two new algorithms for cell image segmentation. First, we demonstrate that pixel classification-based color image segmentation in color space is equivalent to performing segmentation on grayscale image through thresholding. Based on this result, we develop a supervised learning-based two-step procedure for color cell image segmentation, where color image is first mapped to grayscale via a transform learned through supervised learning, thresholding is then performed on the grayscale image to segment objects out of background. Experimental results show that the supervised learning-based two-step procedure achieved a boundary disagreement (mean absolute distance) of 0.85 while the disagreement produced by the pixel classification-based color image segmentation method is 3.59. Second, we develop a new marker detection algorithm for watershed-based separation of overlapping or touching cells. The merit of the new algorithm is that it employs both photometric and shape information and combines the two naturally in the framework of pattern classification to provide more reliable markers. Extensive experiments show that the new marker detection algorithm achieved 0.4% and 0.2% over-segmentation and under-segmentation, respectively, while reconstruction-based method produced 4.4% and 1.1% over-segmentation and under-segmentation, respectively.

Segmentation of medical images using a genetic algorithm

Abstract: Segmentation of medical images is challenging due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Consequently, this task involves incorporating as much prior information as possible (e.g., texture, shape, and spatial location of organs) into a single framework. In this paper, we present a genetic algorithm for automating the segmentation of the prostate on two-dimensional slices of pelvic computed tomography (CT) images. In this approach the segmenting curve is represented using a level set function, which is evolved using a genetic algorithm (GA). Shape and textural priors derived from manually segmented images are used to constrain the evolution of the segmenting curve over successive generations.We review some of the existing medical image segmentation techniques. We also compare the results of our algorithm with those of a simple texture extraction algorithm (Laws' texture measures) as well as with another GA-based segmentation tool called GENIE. Our preliminary tests on a small population of segmenting contours show promise by converging on the prostate region. We expect that further improvements can be achieved by incorporating spatial relationships between anatomical landmarks, and extending the methodology to three dimensions.

Image Segmentation Based on Watershed and Edge Detection Techniques

Abstract: A combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map was used to perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain an accurate edge maps of our images without using watershed method. In this paper: We solved the problem of undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image and the final edge detection result is one closed boundary per actual region in the image.

Range image segmentation using surface selection criterion

Abstract: In this paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. First, and in order to solve the model selection problem, we introduce a novel criterion, which is based on minimising strain energy of fitted surfaces. We then evaluate its performance and compare it with many other existing model selection techniques. Using this criterion, we then present a robust range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The presented algorithm simultaneously identifies the type (order and geometric shape) of each surface and separates all the points that are part of that surface. This paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces. The resulting segmentation algorithm successfully segments various possible types of curved objects. More importantly, the new technique is capable of detecting the association between separated parts of a surface, which has the same Cartesian equation while segmenting a scene. This aspect is very useful in some industrial applications of range data analysis.

Stereoscopic image display method and apparatus, method for generating 3d image data from a 2d image data input and an apparatus for generating 3d image data from a 2d image data input

Abstract: 2D image data are converted into 3D image data. The image is divided, on the basis of focusing characteristics, into two or more regions, it is determined to which region an edge separating two regions belongs. The regions are depth ordered in accordance with the rule that the rule that a region comprising an edge is closer to the viewer than an adjacent region and to the regions 3-D depth information is assigned in accordance with the established depth order of the regions. Preferably to each of the regions a depth is assigned in dependence on an average or median focusing characteristic of the region.

A Real-Time Adaptive Thresholding for Video Change Detection

Abstract: A real-time adaptive non-parametric thresholding algorithm for change detection is proposed in this paper. Based on the estimation of the scatter of regions of change in a difference image, a threshold of each image block is computed discriminatively, then the global threshold is obtained by averaging all the thresholds for image blocks. The block threshold is calculated differently for regions of change and background. Experimental results show the proposed thresholding algorithm performs well for change detection with high efficiency.

Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images

Abstract: This paper presents a new representation and evaluation procedure of page segmentation algorithms and analyzes six widely-used layout analysis algorithms using the procedure. The method permits a detailed analysis of the behavior of page segmentation algorithms in terms of over- and undersegmentation at different layout levels, as well as determination of the geometric accuracy of the segmentation. The representation of document layouts relies on labeling each pixel according to its function in the overall segmentation, permitting pixel-accurate representation of layout information of arbitrary layouts and allowing background pixels to be classified as "don’t care". Our representations can be encoded easily in standard color image formats like PNG, permitting easy interchange of segmentation results and ground truth.

Image segmentation using spatial-color gaussian mixture models

Abstract: A spatial-color Gaussian mixture model (SCGMM) image segmentation technique for segmenting images. The SCGMM image segmentation technique specifies foreground objects in the first frame of an image sequence, either manually or automatically. From the initial segmentation, the SCGMM segmentation system learns two spatial-color Gaussian mixture models (SCGMM) for the foreground and background objects. These models are built into a first-order Markov random field (MRF) energy function. The minimization of the energy function leads to a binary segmentation of the images in the image sequence, which can be solved efficiently using a conventional graph cut procedure.

Method and apparatus for image segmentation

Abstract: A 3D image may be segmented based on one or more intensity thresholds determined from a subset of the voxels in the 3D image. The subset may contain voxels in a 2D reference slice. A low threshold and a high threshold may be used for segmenting an image, and they may be determined using different thresholding methods, depending on the image type. In one method, two sets of bordering pixels are selected from an image. A statistical measure of intensity of each set of pixels is determined. An intensity threshold value is calculated from the statistical measures for segmenting the image. In another method, the pixels of an image are clustered into clusters of different intensity ranges. An intensity threshold for segmenting the image is calculated as a function of a mean intensity and a standard deviation for pixels in one of the clusters. A further method is a supervised range-constrained thresholding method.

Representing and reconstructing high dynamic range images

Abstract: A high dynamic range image can be recovered from a full-resolution lower-dynamic-range image and a reduced-resolution higher-dynamic-range image. Information regarding higher spatial frequencies may be obtained by extracting high spatial frequencies from the lower-dynamic-range image. In some embodiments an approximate impulse-response function is determined by comparing the higher- and lower-dynamic range images. A scaling image obtained by applying the impulse-response function to a high-frequency band of the lower-dynamic range image is combined with an upsampled higher-dynamic range image to yield a reconstructed image.

A Metric Approach to Vector-Valued Image Segmentation

Abstract: We address the issue of low-level segmentation of vector-valued images, focusing on the case of color natural images The proposed approach relies on the formulation of the problem in the metric framework, as a Voronoi tessellation of the image domain In this context, a segmentation is determined by a distance transform and a set of sites Our method consists in dividing the segmentation task in two successive sub-tasks: pre-segmentation and hierarchical representation We design specific distances for both sub-problems by considering low-level image attributes and, particularly, color and lightness information Then, the interpretation of the metric formalism in terms of boundaries allows the definition of a soft contour map that has the property of producing a set of closed curves for any threshold Finally, we evaluate the quality of our results with respect to ground-truth segmentation data

Context-based segmentation of image sequences

Abstract: We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data.

A Novel Graphical Model Approach to Segmenting Cell Images

Abstract: Successful biological image analysis usually requires satisfactory segmentations to identify regions of interest as an intermediate step. Here we present a novel graphical model approach for segmentation of multi-cell yeast images acquired by fluorescence microscopy. Yeast cells are often clustered together, so they are hard to segment by conventional techniques. Our approach assumes that two parallel images are available for each field: an image containing information about the nuclear positions (such as an image of a DNA probe) and an image containing information about the cell boundaries (such as a differential interference contrast, or DIC, image). The nuclear information provides an initial assignment of whether each pixel belongs to the background or one of the cells. The boundary information is used to estimate the probability that any two pixels in the graph are separated by a cell boundary. From these two kinds of information, we construct a graph that links nearby pairs of pixels, and seek to infer a good segmentation from this graph. We pose this problem as inference in a Bayes network, and use a fast approximation approach to iteratively improve the estimated probability of each class for each pixel. The resulting algorithm can efficiently generate segmentation masks which are highly consistent with hand-labeled data, and results suggest that the work will be of particular use for large scale determination of protein location patterns by automated microscopy.

Segmentation by Level Sets and Symmetry

Abstract: Shape symmetry is an important cue for image understanding. In the absence of more detailed prior shape information, segmentation can be significantly facilitated by symmetry. However, when symmetry is distorted by perspectivity, the detection of symmetry becomes non-trivial, thus complicating symmetry-aided segmentation. We present an original approach for segmentation of symmetrical objects accommodating perspective distortion. The key idea is the use of the replicative form induced by the symmetry for challenging segmentation tasks. This is accomplished by dynamic extraction of the object boundaries, based on the image gradients, gray levels or colors, concurrently with registration of the image symmetrical counterpart (e.g. reflection) to itself. The symmetrical counterpart of the evolving object contour supports the segmentation by resolving possible ambiguities due to noise, clutter, distortion, shadows, occlusions and assimilation with the background. The symmetry constraint is integrated in a comprehensive level-set functional for segmentation that determines the evolution of the delineating contour. The proposed framework is exemplified on various images of skewsymmetrical objects and its superiority over state of the art variational segmentation techniques is demonstrated.

Vector Field Convolution for Image Segmentation using Snakes

Abstract: Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new design for the snake external force, called vector field convolution (VFC), to address these problems. Qualitative and quantitative comparisons with the gradient vector flow (GVF) external force are presented in this paper to show the advantages of this innovation.

Automatic video segmentation using spatiotemporal T-junctions

Abstract: The problem of figure‐ground segmentation is of great importance in both video editing and visual perception tasks. Classical video segmentation algorithms approach the problem from one of two perspectives. At one extreme, global approaches constrain the camera motion to simplify the image structure. At the other extreme, local approaches estimate motion in small image regions over a small number of frames and tend to produce noisy signals that are difficult to segment. With recent advances in image segmentation showing that sparse information is often sufficient for figure‐ ground segmentation it seems surprising then that with the extra temporal information of video, an unconstrained automatic figure‐ground segmentation algorithm still eludes the research community. In this paper we present an automatic video segmentation algorithm that is intermediate between these two extremes and uses spatiotemporal features to regularize the segmentation. Detecting spatiotemporal T-junctions that indicate occlusion edges, we learn an occlusion edge model that is used within a colour contrast sensitive MRF to segment individual frames of a video sequence. T-junctions are learnt and classified using a support vector machine and a Gaussian mixture model is fitted to the (foreground, background) pixel pairs sampled from the detected T-junctions. Graph cut is then used to segment each frame of the video showing that sparse occlusion edge information can automatically initialize the video segmentation problem.

Adaptive Image Segmentation Based on Fast Thresholding and Image Merging

Abstract: Image segmentation is the first essential and important step of low level vision. This paper proposes a novel algorithm for adaptive image segmentation, based on thresholding technique and segments merging according to their characteristics combine with spatial position. Our earlier work of getting the entire information of the histogram could help choose the multiple thresholds. However, not all the peaks of the histogram correspond to obvious structural unit in the image. Spatial information must be involved. This paper also suggests subjoining segments matching for video image tracking. They will give great help to image segmentation. The proposed algorithm can meet the real-time requirement and lead to higher segmentation accuracy, some types of texture can also be segmented well; it can be applied in many conditions, including complex target segmented. We describe the algorithm in detail and perform simulation experiments. The computation based on pixels can fully parallel processing to save time.

Segmentation of digital images

Abstract: A method and system for segmenting a digital image, the digital image comprising at least some mixed pixels whose visual characteristics are determined by a mixture of the visual characteristics of part of two or more portions of the image A method comprises the steps of: selecting one or more pixels within a first portion of the image to define a first pixel selection; expanding the first pixel selection to define a second pixel selection corresponding to a first portion of the image; selecting one or more pixels within a second portion of the image to define a third pixel selection; expanding the third pixel selection to define a fourth pixel selection corresponding to a second portion of the image; making a determination as to how close together the second pixel selection and the fourth pixel selection are; and indicating to a user whether or not the second pixel selection and fourth pixel selection are sufficiently close that the pixels occurring in between the second pixel selection and the fourth pixel selection are pixels in a boundary portion of the image

Image processing apparatus, control method thereof, and program

Abstract: A binary image is generated by binarizing a multilevel image. An edge image is generated by extracting an edge component in the multilevel image. The binary image is segmented into a plurality of regions with different attributes. An outline candidate of a halftone region is extracted from the edge image. A second region segmentation result is output on the basis of the information of the outline candidate and information of the region segmentation result.

Uni-Modal Versus Joint Segmentation for Region-Based Image Fusion

Abstract: A number of segmentation techniques are compared with regard to their usefulness for region-based image and video fusion. In order to achieve this, a new multi-sensor data set is introduced containing a variety of infra-red, visible and pixel fused images together with manually produced "ground truth" segmentations. This enables the objective comparison of joint and unimodal segmentation techniques. A clear advantage to using joint segmentation over unimodal segmentation, when dealing with sets of multi-modal images, is shown. The relevance of these results to region-based image fusion is confirmed with task-based analysis and a quantitative comparison of the fused images produced using the various segmentation algorithms.

Image processing method, three-dimensional position measuring method and image processing apparatus

Abstract: An object of this invention is to provide an image processing method and image processing apparatus capable of extracting the position of an object in the unit of sub-pixel. An edge pixel, density gradient direction and edge position in the unit of sub-pixel are extracted from a model image obtained by photographing a high-quality model of the object and registered in a hard disk. After a processing object image is inputted, CPU obtains a position of an image region corresponding to the model image and after that, extracts an edge pixel whose edge position in the unit of sub-pixel is related to a corresponding pixel on the model image side in that image region. The edge position in the unit of sub-pixel is extracted for these edge pixels and the quantity of deviation between the extracting position and the edge position on the model image side is calculated. The position in the unit of pixel is corrected with an average value of the quantity of deviation obtained for each edge pixel so as to obtain a real position of the object.

Edge Based Text Detection for Multi-purpose Application

Abstract: Text detection plays a crucial role in various applications. In this paper we present an edge based text detection technique in the complex images for multi purpose application. The technique applied vertical Sobel edge detection and a newly proposed morphological technique that used to connect the edges to form the candidate regions. The technique has special advantage, by providing a distinguishable texture on the text area over the others. The connected components are then extracted using a purposed segmentation algorithm. Later all the candidate regions are verified to specify the text region. The propose techniques has been tested with different types of image acquired from different input sources and environment. The experimental result shows highly successful rate

A general framework for image segmentation using ordered spatial dependency

Abstract: The segmentation problem appears in most medical imaging applications. Many research groups are pushing toward a whole body segmentation based on atlases. With a similar objective, we propose a general framework to segment several structures. Rather than inventing yet another segmentation algorithm, we introduce inter-structure spatial dependencies to work with existing segmentation algorithms. Ranking the structures according to their dependencies, we end up with a hierarchical approach that improves each individual segmentation and provides automatic initializations. The best ordering of the structures can be learned off-line. We apply this framework to the segmentation of several structures in brain MR images.

Region Based Segmentation Using the Tree of Shapes

Abstract: The tree of shapes is a powerful tool for image representation which holds many interesting properties. Many works in the literature use it for image segmentation, but most of them use only boundary information along the level lines. In many real images this is not enough to achieve a good segmentation, and region information must be introduced. In this work we present a novel region-based segmentation algorithm using the tree of shapes. The approach taken consists in the selection of relevant level-lines according to region based descriptors computed from their interior. We describe a region using the histogram of its features and we select interesting regions by identifying parts of the tree with an homogeneous histogram. The main contribution of this work is the joint use of histograms and suitable metrics between them, with the powerful representation of the tree of shapes. This allows us to handle complex region models and thus improves on previous works which were only able to deal with piecewise constant models. We validate our approach with real images and we obtain results which are favorably compared with some well known related approaches.

Matching 2d image segments with genetic algorithms and approximation spaces

Abstract: This article introduces an approach to matching 2D image segments using approximation spaces. The rough set approach introduced by Zdzislaw Pawlak provides a ground for concluding to what degree a particular set of similar image segments is a part of a set of image segments representing a norm or standard. The number of features (color difference and overlap between segments) typically used to solve the image segment matching problem is small. This means that there is not enough information to permit image segment matching with high accuracy. By contrast, many more features can be used in solving the image segment matching problem using a combination of evolutionary and rough set methods. Several different uses of a Darwinian form of a genetic algorithm (GA) are introduced as a means to partition large collections of image segments into blocks of similar image segments. After filtering, the output of a GA provides a basis for finding matching segments in the context of an approximation space. A coverage form of approximation space is presented in this article. Such an approximation space makes it possible to measure the the extent that a set of image segments representing a standard covers GA-produced blocks. The contribution of this article is the introduction of an approach to matching image segments in the context of an approximation space.

Shape-Based Image Segmentation Using Normalized Cuts

Abstract: To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.

Concurrent 3-D motion segmentation and 3-D interpretation of temporal sequences of monocular images

Abstract: The purpose of this study is to investigate a variational method for joint multiregion three-dimensional (3-D) motion segmentation and 3-D interpretation of temporal sequences of monocular images. Interpretation consists of dense recovery of 3-D structure and motion from the image sequence spatiotemporal variations due to short-range image motion. The method is direct insomuch as it does not require prior computation of image motion. It allows movement of both viewing system and multiple independently moving objects. The problem is formulated following a variational statement with a functional containing three terms. One term measures the conformity of the interpretation within each region of 3-D motion segmentation to the image sequence spatiotemporal variations. The second term is of regularization of depth. The assumption that environmental objects are rigid accounts automatically for the regularity of 3-D motion within each region of segmentation. The third and last term is for the regularity of segmentation boundaries. Minimization of the functional follows the corresponding Euler-Lagrange equations. This results in iterated concurrent computation of 3-D motion segmentation by curve evolution, depth by gradient descent, and 3-D motion by least squares within each region of segmentation. Curve evolution is implemented via level sets for topology independence and numerical stability. This algorithm and its implementation are verified on synthetic and real image sequences. Viewers presented with anaglyphs of stereoscopic images constructed from the algorithm's output reported a strong perception of depth.

Methods and Systems for Segmenting a Digital Image into Regions

Abstract: Aspects of the present invention relate to methods and systems for segmenting a digital image into regions. A frequency-of-occurrence of image values may be determined excluding a portion of pixels in a digital image. An image value associated with a peak in the frequency-of-occurrence of image values may be identified and associated with an image label. Pixel locations may be labeled based on the associated labels and image values. Additionally, unreliable pixels may be determined and labeled based on the associated labels and image values, and an unreliable pixel may be assigned a label after multiple scan passes of a classification map.