Clustering on Local Appearance for Deformable Model Segmentation

Stough, Joshua V.; Broadhurst, Robert E.; Pizer, Stephen M.; Chaney, Edward L.

doi:10.1109/isbi.2007.357013

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The impetus for the local-clustered appearance model [11] is that more local regions will better specify the exterior than the common single homogeneous region approach. The question is what constitutes a region.…”

Section: Local-clustered Regionsmentioning

confidence: 99%

Regional Appearance in Deformable Model Segmentation

Stough

Broadhurst

Pizer

et al. 2007

Lecture Notes in Computer Science

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Abstract. Automated medical image segmentation is a challenging task that benefits from the use of effective image appearance models. In this paper, we compare appearance models at three regional scales for statistically characterizing image intensity near object boundaries in the context of segmentation via deformable models. The three models capture appearance in the form of regional intensity quantile functions. These distribution-based regional image descriptors are amenable to Euclidean methods such as principal component analysis, which we use to build the statistical appearance models.The first model uses two regions, the interior and exterior of the organ of interest. The second model accounts for exterior inhomogeneity by clustering on object-relative local intensity quantile functions to determine tissue-consistent regions relative to the organ boundary. The third model analyzes these image descriptors per geometrically defined local region.To evaluate the three models, we present segmentation results on bladders and prostates in CT in the context of day-to-day adaptive radiotherapy for the treatment of prostate cancer. Results show improved segmentations with more local regions, probably because smaller regions better represent local inhomogeneity in the intensity distribution near the organ boundary.

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Section: Local-clustered Regionsmentioning

confidence: 99%

Regional Appearance in Deformable Model Segmentation

Stough

Broadhurst

Pizer

et al. 2007

Lecture Notes in Computer Science

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“…Deformable model techniques are other techniques that are used for segmentation [ 17 , 18 ]. These techniques use closed parametric curves or surfaces that deform under the influence of internal and external forces.…”

Section: Introductionmentioning

confidence: 99%

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Davuluri

Tang

et al. 2012

Computational and Mathematical Methods in Medicine

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Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising.

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Image Segmentation and Analysis for Automated Classification of Traumatic Pelvic Injuries

Najarian

Vasilache

2010

VCU Theses and Dissertations

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Clustering on Local Appearance for Deformable Model Segmentation

Cited by 3 publications

References 10 publications

Regional Appearance in Deformable Model Segmentation

Regional Appearance in Deformable Model Segmentation

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Image Segmentation and Analysis for Automated Classification of Traumatic Pelvic Injuries

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