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336
Hybrid Image Segmentation Using Watersheds and Fast Region Merging
 IEEE transactions on Image Processing
, 1998
"... Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est ..."
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Cited by 146 (1 self)
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Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottomup) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the socalled nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, onepixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with twodimensional/threedimensional (2D/3D) magnetic resonance images are presented. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform. I.
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.
Grayscale Area Opening and Closing, their efficient implementation and applications
 in Proc. Eurasip Workshop on Mathematical Morphology and its Applications to Signal Processing
, 1993
"... The filter that removes from a binary image its connected components with area smaller than a parameter is called area opening. From a morphological perspective, this filter is an algebraic opening, and it can be extended to grayscale images. The properties of area openings and their dual area cl ..."
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Cited by 63 (0 self)
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The filter that removes from a binary image its connected components with area smaller than a parameter is called area opening. From a morphological perspective, this filter is an algebraic opening, and it can be extended to grayscale images. The properties of area openings and their dual area closings are recalled. In particular, it was proved in [13] that the area opening of parameter of an image I is the supremum of the grayscale images that are smaller than I and whose regional maxima are of area greater than or equal to . This theorem is at the basis of an efficient algorithm for computing grayscale area openings and closings. Its implementation involves scanning pixels in an order that depends both on their location and value. For this purpose, the use of pixel heaps is proposed. This data structure is shown to be both efficient and low in memory requirements. In addition, it can be used in the computation of various other complex morphological transforms. The use of these area openings and closings is illustrated on image filtering and segmentation tasks 1 Notations, Definitions In this paper, the binary images or sets under study are subsets of a connected compact set M IR2 called the mask. Similarly, grayscale images are mappings from M to IR. For simplicity, only the 2D case is considered here, but the notions and algorithms discussed generalize to arbitrary dimensions. By area is meant the Lebesgue measure in IR2. B denotes the morphological opening with respect to structuring element B. Let us first recall the notion of connected openings
A Comparison of Algorithms for Connected Set Openings and Closings
 IEEE TRANS. PATT. ANAL. MACH. INTELL
, 2002
"... The implementation of morphological connected set operators for image filtering and pattern recognition is discussed. Two earlier algorithms based on priority queues and hierarchical queues, respectively, are compared to a more recent unionfind approach. Unlike the earlier ..."
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Cited by 60 (12 self)
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The implementation of morphological connected set operators for image filtering and pattern recognition is discussed. Two earlier algorithms based on priority queues and hierarchical queues, respectively, are compared to a more recent unionfind approach. Unlike the earlier
Automated segmentation, classification, and tracking of cancer cell nuclei in timelapse microscopy
 IEEE Transactions on Biomedical Engineering
, 2006
"... Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in timelapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditi ..."
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Cited by 50 (5 self)
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Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in timelapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such timelapse datasets, and manual analysis is unreasonably timeconsuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification. Key Words: timelapse fluorescence microscopy; image analysis; phase identification; tracking
Learning Color Names for RealWorld Applications
"... Color names are required in realworld applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a welldefined experimental setup by human test subjects. However naming ..."
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Cited by 44 (11 self)
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Color names are required in realworld applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a welldefined experimental setup by human test subjects. However naming colors in realworld images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from realworld images. To avoid hand labelling realworld images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from realworld images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation. I.
Power Watershed: A Unifying GraphBased Optimization Framework
, 2011
"... In this work, we extend a common framework for graphbased image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of ..."
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Cited by 42 (8 self)
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In this work, we extend a common framework for graphbased image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watershed in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watershed. In particular when q = 2, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasilinear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.
SignificanceLinked Connected Component Analysis for Very Low Bit Rate Wavelet Video Coding
 IEEE Transactions on Image Processing
, 1998
"... In recent years, a tremendous success in wavelet image coding has been achieved. It is mainly attributed to innovative strategies for data organization and representation of wavelettransformed images. However, there have been only a few successful attempts in wavelet video coding. The most successf ..."
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Cited by 37 (10 self)
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In recent years, a tremendous success in wavelet image coding has been achieved. It is mainly attributed to innovative strategies for data organization and representation of wavelettransformed images. However, there have been only a few successful attempts in wavelet video coding. The most successful one is perhaps Sarnoff Corporation's zerotree entropy (ZTE) video coder. In the paper, a novel hybrid wavelet video coding algorithm termed video significancelinked connected component analysis (VSLCCA) is developed for very low bit rate applications. It has also been empirically evidenced that wavelet transform combined with those innovative data organization and representation strategies can be an invaluable asset in very low bit rate video coding as long as motioncompensated error frames are ensured to be blockingeffectfree or coherent. In the proposed VSLCCA codec, first, finetuned motion estimation based on H.263 Recommendation is developed to reduce temporal redundancy and exha...
Gradient Watersheds in Morphological ScaleSpace
 IEEE Transactions on Image Processing
, 1996
"... this paper. ..."
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