3-D segmentation of MR images of the head for 3-D display

Bomans, M.; Höhne, Karl Heinz; Tiede, Ulf; Riemer, Martin

doi:10.1109/42.56342

Cited by 253 publications

(87 citation statements)

References 14 publications

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“…The final surface is modelled with a "snake" [Kass et al, 1987]. Further examples can be found in [Bomans et al, 1990;Brummer et al, 1993;Kruggel and von Cramen, 1999].…”

Section: Reviewmentioning

confidence: 99%

Fast robust automated brain extraction

Smith

2002

Human Brain Mapping

9,609

6,756

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An automated method for segmenting magnetic resonance head images into brain and nonbrain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Hum. Brain Mapping 17:143-155, 2002.

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“…The final surface is modelled with a "snake" [Kass et al, 1987]. Further examples can be found in [Bomans et al, 1990;Brummer et al, 1993;Kruggel and von Cramen, 1999].…”

Section: Reviewmentioning

confidence: 99%

Fast robust automated brain extraction

Smith

2002

Human Brain Mapping

9,609

6,756

View full text Add to dashboard Cite

show abstract

“…Surface rendering, for example, is a commonly used rendering method for displaying external structures and object shapes. 6,7 Volumetric rendering methods are more diagnostically relevant for revealing internal anatomical structures. 8,9 One of the most commonly used volumetric rendering methods is maximum intensity projection (MIP), which maximizes contrast on a rendered image by assigning pixels the brightest voxel along projections through the 3D dataset.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Radiologists’ Search Strategies for Lung Nodule Detection: Slice-Based Versus Volumetric Displays

Wang

Durick

et al. 2007

J Digit Imaging

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The goal of this study was to assess whether radiologists' search paths for lung nodule detection in chest computed tomography (CT) between different rendering and display schemes have reliable properties that can be exploited as an indicator of ergonomic efficiency for the purpose of comparing different display paradigms. Eight radiologists retrospectively viewed 30 lung cancer screening CT exams, containing a total of 91 nodules, in each of three display modes [i.e., slice-by-slice, orthogonal maximum intensity projection (MIP) and stereoscopic] for the purpose of detecting and classifying lung nodules. Radiologists' search patterns in the axial direction were recorded and analyzed along with the location, size, and shape for each detected feature, and the likelihood that the feature is an actual nodule. Nodule detection performance was analyzed by employing free-response receiver operating characteristic methods. Search paths were clearly different between sliceby-slice displays and volumetric displays but, aside from training and novelty effects, not between MIP and stereographic displays. Novelty and training effects were associated with the stereographic display mode, as evidenced by differences between the beginning and end of the study. The stereo display provided higher detection and classification performance with less interpretation time compared to other display modes tested in the study; however, the differences were not statistically significant. Our preliminary results indicate a potential role for the use of radiologists' search paths in evaluating the relative ergonomic efficiencies of different display paradigms, but systematic training and practice is necessary to eliminate training curve and novelty effects before search strategies can be meaningfully compared.

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“…As introduced in Fig 1, the image denoted by 1 I is divided into hundreds of regions after improved watershed transform. The following are the fast region combination, an important aspect of which is how to define the similarities (We call it region cost)…”

Section: Fast Region Merging Algorithmmentioning

confidence: 99%

Region-based Image Segmentation by Watershed Partition and DCT Energy Compaction

Pun¹,

An²,

Chen³

2012

IJCIS

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An image segmentation approach by improved watershed partition and DCT energy compaction has been proposed in this paper. The proposed energy compaction, which expresses the local texture of an image area, is derived by exploiting the discrete cosine transform. The algorithm is a hybrid segmentation technique which is composed of three stages. First, the watershed transform is utilized by preprocessing techniques: edge detection and marker in order to partition the image in to several small disjoint patches, while the region size, mean and variance features are used to calculate region cost for combination. Then in the second merging stage the DCT transform is used for energy compaction which is a criterion for texture comparison and region merging. Finally the image can be segmented into several partitions. The experimental results show that the proposed approach achieved very good segmentation robustness and efficiency, when compared to other state of the art image segmentation algorithms and human segmentation results.

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3-D segmentation of MR images of the head for 3-D display

Cited by 253 publications

References 14 publications

Fast robust automated brain extraction

Fast robust automated brain extraction

Characterization of Radiologists’ Search Strategies for Lung Nodule Detection: Slice-Based Versus Volumetric Displays

Region-based Image Segmentation by Watershed Partition and DCT Energy Compaction

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