Parallel Marker-Based Image Segmentation with Watershed Transformation

Moga, Alina N.; Gabbouj, Moncef

doi:10.1006/jpdc.1998.1448

Cited by 44 publications

(26 citation statements)

References 11 publications

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“…3c). As estimation of the markers improves the efficiency of watershed segmentation algorithm (Moga & Gabbouj, 1998), markers were detected on a post processed image in this study.…”

Section: Marker Detectionmentioning

confidence: 99%

Microscope-based computer vision to characterize soil texture and soil organic matter

Sudarsan

Biswas

et al. 2016

Biosystems Engineering

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“…3c). As estimation of the markers improves the efficiency of watershed segmentation algorithm (Moga & Gabbouj, 1998), markers were detected on a post processed image in this study.…”

Section: Marker Detectionmentioning

confidence: 99%

Microscope-based computer vision to characterize soil texture and soil organic matter

Sudarsan

Biswas

et al. 2016

Biosystems Engineering

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“…The watershed algorithm [4] is widely accepted for touching object segmentation and successfully used in segmenting histopathology images [13]. We compared our method with watershed using the 207 touching cell image dataset and listed the results in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm is sensitive to noise and often produces many oversegmented small regions. Marker-based watershed [4] can partially remedy this issue, but it requires manual selection or accurate estimation of the markers.…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph

Yang

Tuzel

Meer

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Automatic image analysis of histopathology specimens would help the early detection of blood cancer. The first step for automatic image analysis is segmentation. However, touching cells bring the difficulty for traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. Robust estimation and color active contour models are used to delineate the outer boundary. Concave points on the boundary and inner edges are automatically detected. A concave vertex graph is constructed from these points and edges. By minimizing a cost function based on morphological characteristics, we recursively calculate the optimal path in the graph to separate the touching cells. The algorithm is computationally efficient and has been tested on two large clinical dataset which contain 207 images and 3898 images respectively. Our algorithm provides better results than other studies reported in the recent literature.

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“…The proposed parallel framework guarantees that the processors are not tightly synchronized [23]. In addition, the processors execute a similar amount of work at approximately the same time, thus achieving load balance.…”

Section: Parallel Morphological Watershed-based Classification Algorithmmentioning

confidence: 99%

Morphological Hyperspectral Image Classification: A Parallel Processing Perspective

Plaza

2006

Hyperspectral Data Exploitation

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Mathematical morphology (MM) is a theory for spatial structure analysis that was established by introducing fundamental operators applied to two sets [1]. A set is processed by another one having a carefully selected shape and size, known as the structuring element (SE). In the context of image processing, the SE acts as a probe for extracting or suppressing specific structures of the image objects, checking that each position of the SE fits within those objects. Based on these ideas, two fundamental operators are defined in MM, namely erosion and dilation. The application of the erosion operator to an image yields an output image, which shows where the SE fits the objects in the image. On the other hand, the application of the dilation operator to an image produces an output image, which shows where the SE hits the objects in the image. All other MM operations can be expressed in terms of erosion and dilation [2]. For instance, the notion behind the opening operator is to dilate an eroded image in order to recover as much as possible of the eroded image. In contrast, the closing operator erodes a dilated image so as to recover the initial shape of image structures that have been dilated. The filtering properties of the opening and closing are based on the fact that, depending on the size and shape of the considered SE, not all structures from the original image will be recovered when these operators are applied. Because of the nonlinear properties of MM filters, their application generally results in an irreversible, though controlled, loss of information.Although MM operators were originally defined for binary images, they were soon extended to gray-tone (mono-channel) images by viewing these data as an imaginary topographic relief in which the brighter the gray tone, the higher the corresponding elevation [3]. Here, morphological operations can be graphically 353interpreted as the result of sliding a flat SE over the topographical relief, so that the SE defines the new (dilated or eroded) scene based on its spatial properties such as height or width (see Figure 13.1). However, extension of MM operators to multichannel data such as hyperspectral imagery with hundreds of spectral channels is not straightforward. A simple approach consists in applying grayscale MM techniques to each channel separately, an approach that has been called marginal MM in the literature [4]. However, the marginal approach is often unacceptable in remote sensing applications because, when MM techniques are applied independently to each image channel, analysis techniques are subject to the well-known problem of ''false colors''; that is, it is very likely that new spectral constituents not present in the original image may be created as a result of processing the channels separately [5]. An alternative way to approach the problem of multichannel MM is to treat the data at each pixel as a vector. Unfortunately, there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension, and th...

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Parallel Marker-Based Image Segmentation with Watershed Transformation

Cited by 44 publications

References 11 publications

Microscope-based computer vision to characterize soil texture and soil organic matter

Microscope-based computer vision to characterize soil texture and soil organic matter

Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph

Morphological Hyperspectral Image Classification: A Parallel Processing Perspective

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