Morphological segmentation

Meyer, Fernand; Beucher, Serge

doi:10.1016/1047-3203(90)90014-m

Cited by 1,081 publications

(687 citation statements)

References 2 publications

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“…First, seed points were placed within the axons to initiate a watershed-based segmentation (Beucher and Lantuejoul, 1979;Meyer and Beucher, 1990) of the ICS. The myelin was then segmented using an interactive profile-based boundary detection algorithm (Pal and Pal, 1993).…”

Section: Synthetic Axon Images and Optical Images Of Mouse Spinal Cordmentioning

confidence: 99%

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

et al. 2008

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Q-space imaging (QSI), a diffusion MRI technique, can provide quantitative tissue architecture information at cellular dimensions not amenable by conventional diffusion MRI. By exploiting regularities in molecular diffusion barriers, QSI can estimate the average barrier spacing such as the mean axon diameter in white matter (WM). In this work, we performed ex vivo QSI on cervical spinal cord sections from healthy C57BL/6 mice at 400MHz using a custom-designed uniaxial 50T/m gradient probe delivering a 0.6 µm displacement resolution capable of measuring axon diameters on the scale of 1 µm. After generating QSI-derived axon diameter maps, diameters were calculated using histology from seven WM tracts (dorsal corticospinal, gracilis, cuneatus, rubrospinal, spinothalamic, reticulospinal, and vestibulospinal tracts) each with different axon diameters. We found QSI-derived diameters from regions drawn in the seven WM tracts (1.1 to 2.1 µm) to be highly correlated (r 2 = 0.95) with those calculated from histology (0.8 to 1.8 µm). The QSI-derived values overestimated those obtained by histology by approximately 20%, which is likely due to the presence of extracellular signal. Finally, simulations on images of synthetic circular axons and axons from histology suggest that QSI-derived diameters are informative despite diameter and axon shape variation and the presence of intra-cellular and extra-cellular signal. QSI may be able to quantify nondestructively changes in WM axon architecture due to pathology or injury at the cellular level.

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Section: Synthetic Axon Images and Optical Images Of Mouse Spinal Cordmentioning

confidence: 99%

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

et al. 2008

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“…There have been improvements in the original watershed transform in order to reduce its drawbacks. These include using marker-controlled watershed paradigm [23] as well as hierarchical watershed paradigms such as the waterfall algorithm [24].…”

Section: Introductionmentioning

confidence: 99%

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

López-Mir

Naranjo

Angulo

et al. 2014

Computer Methods and Programs in Biomedicine

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There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of .91 ± .02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

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“…Despite many years of research, unsupervised image segmentation techniques without human interaction still do not produce satisfactory results [10]. Therefore semi-supervised segmentation methods incorporating user interactions have been proposed [11,12,13,14,15] and are becoming more and more popular. For instance, in the active contour model (ACM) i.e.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

Saxena¹,

Jain²,

Singh³

2011

SpecialIssue

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Abstract-In this work; we address a novel interactive framework for object retrieval using unsupervised similar region merging and flood fill method which models the spatial and appearance relations among image pixels. Efficient and effective image segmentation is usually very hard for natural and complex images. This paper presents a new technique for similar region merging and objects retrieval. The users only need to roughly indicate the after which steps desired objects boundary is obtained during merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique. A region R is merged with its adjacent regions Q if Q has highest similarity with R among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 22 object classes (524 images total) show promising results.

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Morphological segmentation

Cited by 1,081 publications

References 2 publications

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

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