Double Contour Active Shape Models

Seise, Matthias; McKenna, Stephen J.; Ricketts, Ian W.; Wigderowitz, C.

doi:10.5244/c.19.21

Cited by 4 publications

(3 citation statements)

References 15 publications

(19 reference statements)

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“…To the best of our knowledge, only the studies by Anthony et al [1] and Shamir et al [22] focused on knee joint area localization. While the problem of knee joint area localization can be implicitly solved by annotating the anatomical bone landmarks using deformable models [21,15], it would be unfeasible to perform large-scale studies since the use of these algorithms is computationally costly. Currently, despite the presence of multiple approaches, no studies have reported so far their applicability among different datasets.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs

et al. 2017

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Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computeraided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tackle the issue. Here, three different datasets of knee radiographs were used (n = 473/93/77) to validate the overall performance of the method. Our pipeline consists of two parts: anatomically-based joint area proposal and their evaluation using Histogram of Oriented Gradients and the pre-trained Support Vector Machine classifier scores. The obtained results for the used datasets show the mean intersection over the union equals to: 0.84, 0.79 and 0.78. Using a high-end computer, the method allows to automatically annotate conventional knee radiographs within 14-16ms and high resolution ones within 170ms. Our results demonstrate that the developed method is suitable for large-scale analyses.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs

et al. 2017

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“…The method was evaluated on a data set of 30 standard clinical x-rays (see also [10]). Images of left knees were mirrored so that they appeared as right knees.…”

Section: Methodsmentioning

confidence: 99%

“…In medical applications, especially, it is desirable to have an indication of the certainty of a measurement and to cope with structures that overlap or are in close proximity. The proposed method is evaluated for the segmentation of the knee joint for the purpose of the measurement of joint space which is an important biomarker for the assessment of osteoarthritis [10]. …”

Section: Introductionmentioning

confidence: 99%

Segmenting Multiple Objects with Overlapping Appearance and Uncertainty

Seise

McKenna

Ricketts

et al. 2006

Procedings of the British Machine Vision Conference 2006

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A probabilistic method is proposed for segmentation of multiple objects that overlap or are in close proximity to one another. A likelihood function is formulated that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape) are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior distribution over model parameters from which expectations can be estimated. The method is described in detail for the problem of segmenting femur and tibia in x-ray images. The result is a probabilistic segmentation that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.

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Image Segmentation Based on Two-Dimensional Markov Chains

Medvedeva

Kurbatova

2014

Computer Vision in Control Systems-2

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Double Contour Active Shape Models

Cited by 4 publications

References 15 publications

A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs

A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs

Segmenting Multiple Objects with Overlapping Appearance and Uncertainty

Image Segmentation Based on Two-Dimensional Markov Chains

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