Results 1  10
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62
Improved watershed transform for medical image segmentation using prior information
 IEEE TMI
, 2004
"... Abstract—The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has importan ..."
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Cited by 96 (4 self)
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Abstract—The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation. Index Terms—Biomedical imaging, image segmentation, morphological operations, tissue classification, watersheds.
The image foresting transform: Theory, algorithms, and applications
 IEEE TPAMI
, 2004
"... The image foresting transform (IFT) is a graphbased approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definiti ..."
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Cited by 96 (33 self)
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The image foresting transform (IFT) is a graphbased approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it—a generalization of Dijkstra’s algorithm—with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.
A Graphbased Approach for Multiscale Shape Analysis
, 2003
"... This paper presents the advantages of computing two recently proposed shape descriptors, multiscale fractal dimension and contour saliences, using the image foresting transforma graphbased approach to the design of image processing operators. It introduces a robust approach to estimate contour ..."
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Cited by 48 (20 self)
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This paper presents the advantages of computing two recently proposed shape descriptors, multiscale fractal dimension and contour saliences, using the image foresting transforma graphbased approach to the design of image processing operators. It introduces a robust approach to estimate contour saliences (peaks of high curvature) by exploiting the relation between contour and skeleton. The paper also compares both shape descriptors to fractal dimension, Fourier descriptors, and moment invariants with respect to their invariance to object characteristics that belong to a same class (compactability) and to their discriminatory ability to separate objects that belong to distinct classes (separability).
Implementation and Complexity of the WatershedfromMarkers Algorithm Computed as a Minimal Cost Forest
 Computer Graphics Forum
, 2001
"... The watershed algorithm belongs to classical algorithms in mathematical morphology. Lotufo et al. published a principle of the watershed computation by means of an image foresting transform (IFT), which computes a shortest path forest from given markers. The algorithm itself was described for a 2D c ..."
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Cited by 26 (5 self)
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The watershed algorithm belongs to classical algorithms in mathematical morphology. Lotufo et al. published a principle of the watershed computation by means of an image foresting transform (IFT), which computes a shortest path forest from given markers. The algorithm itself was described for a 2D case (image) without a detailed discussion of its computation and memory demands for real datasets. As IFT cleverly solves the problem of plateaus and as it gives precise results when thin objects have to be segmented, it is obvious to use this algorithm for 3D datasets taking in mind the minimizing of a higher memory consumption for the 3D case without loosing low asymptotical time complexity of O(m+C) (and also the real computation speed). The main goal of this paper is an implementation of the IFT algorithm with a priority queue with buckets and careful tuning of this implementation to reach as minimal memory consumption as possible. The paper presents five possible modifications and methods of implementation of the IFT algorithm. All presented implementations keep the time complexity of the standard priority queue with buckets but the best one minimizes the costly memory allocation and needs only 1945% of memory for typical 3D medical imaging datasets. Memory saving was reached by an IFT algorithm simplification, which stores more elements in temporary structures but these elements are simpler and thus need less memory. The best presented modification allows segmentation of large 3D medical datasets (up to 512x512x680 voxels) with 12 or 16bits per voxel on currently available PC based workstations.
Some links between mincuts, optimal spanning forests and watersheds
, 2007
"... Keywords: Di erent optimal structures: minimum cuts, minimum spanning forests and shortestpath forests, have been used as the basis for powerful image segmentation procedures. The wellknown notion of watershed also falls into this category. In this paper, we present some new results about the link ..."
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Cited by 24 (3 self)
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Keywords: Di erent optimal structures: minimum cuts, minimum spanning forests and shortestpath forests, have been used as the basis for powerful image segmentation procedures. The wellknown notion of watershed also falls into this category. In this paper, we present some new results about the links which exist between these di erent approaches. Especially, we show that mincuts coincide with watersheds for some particular weight functions. mincuts, spanning forests, watersheds, shortestpath forests.
IFTWatershed from GrayScale Marker
, 2002
"... The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we intro ..."
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Cited by 17 (11 self)
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The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we introduce the IFTwatershed from gray scale marker  a method to compute at same time, the reconstruction and the classical watershed transform of the reconstructed image, without explicit computation of any regional minima. The method is based on the Image Foresting Transform (IFT)  a unified and efficient approach to reduce image processing problems to a minimumcost path forest problem in a graph. As additional contributions, we demonstrate that (i) the cost map of the IFTwatershed from markers is identical to the output of the superior gray scale reconstruction; (ii) other reconstruction algorithms are not watersheds; and (iii) the proposed method achieves competitive advantages as compared to the current classical watershed approach. 1
Supervised pattern classification based on optimumpath forest
 INTERN. JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (IJIST
, 2009
"... We present a supervised classification method which represents each class by one or more optimumpath trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototyp ..."
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Cited by 17 (11 self)
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We present a supervised classification method which represents each class by one or more optimumpath trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimumcost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimumpath forest rooted at them. The class of the samples in an optimumpath tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimumpath forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANNMLP, and kNN classifiers in several experiments with datasets of various types.
Contour Salience Descriptors for Effective Image Retrieval and Analysis
 IMAGE AND VISION COMPUTING
, 2007
"... This work exploits the resemblance between contentbased image retrieval and image analysis with respect to the design of image descriptors and their effectiveness. In this context, two shape descriptors are proposed: contour saliences and segment saliences. Contour saliences revisits its original d ..."
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Cited by 16 (10 self)
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This work exploits the resemblance between contentbased image retrieval and image analysis with respect to the design of image descriptors and their effectiveness. In this context, two shape descriptors are proposed: contour saliences and segment saliences. Contour saliences revisits its original definition, where the location of concave points was a problem, and provides a robust approach to incorporate concave saliences. Segment saliences introduces salience values for contour segments, making it possible to use an optimal matching algorithm as distance function. The proposed descriptors are compared with convex contour saliences, curvature scale space, and beam angle statistics using a fish database with 11,000 images organized in 1,100 distinct classes. The results indicate segment saliences as the most effective descriptor for this particular application and confirm the improvement of the contour salience descriptor in comparison with convex contour saliences.
A novel graphical model approach to segmenting cell images
 Proc. IEEE Symp. Comput. Intell. Bioinform. Comput. Biol., (CIBCB
, 2006
"... 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 multicell yeast images acquired by fluorescence microscopy. Yeast cells are often ..."
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Cited by 15 (5 self)
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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 multicell 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 handlabeled data, and results suggest that the work will be of particular use for large scale determination of protein location patterns by automated microscopy. I.