Skeletal growth estimation using radiographic image processing and analysis

Mahmoodi, Sasan; Sharif, B.S.; Chester, E.G.; Owen, Julia P.; Lee, R.

doi:10.1109/4233.897061

Cited by 62 publications

(25 citation statements)

References 16 publications

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“…In the same year 1999 Sasan Mahmoodi et al [47] considered 57 images including males and females and used binary threshold method and concave convex detection methods to remove the soft tissue pixels and to enhance the finger bone boundaries. Later an active shape model is used to detect the edges of the bony pixels by super imposing the trained image on the processing image.…”

Section: Active Shape/active Appearance Modelsmentioning

confidence: 99%

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Rajitha¹,

Agarwal²

2014

IJCA

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Section: Active Shape/active Appearance Modelsmentioning

confidence: 99%

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Rajitha¹,

Agarwal²

2014

IJCA

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“…Active shape models (ASM) [28][29][30] and Active Appearance Models (AAM) 2,9,31 have been used previously for the segmentation of bones from radiographs. However, the use of ASMs for this task has decreased since the introduction of AAMs, as the AAM uses a more complete model of an image.…”

Section: Active Appearance Modelsmentioning

confidence: 99%

On the Segmentation and Classification of Hand Radiographs

Davis

Theobald

Lines

et al. 2012

Int. J. Neur. Syst.

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This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components.

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“…Therefore, alternative approaches are required to describe the nonlinear shape variations of structures organizations. One approach is to apply piecewise linear models (Heap and Hogg, 1997) and the other is to separately model variations of shapes of individual parts and the variations of their spatial relationships (Mahmoodi et al, 2000). The problem with piecewise linearization as described by Heap et al (1997) is that it only approximates the non-linear shape variations because it is not using a priori knowledge on the organization of structures.…”

Section: Introductionmentioning

confidence: 99%

“…The problem with piecewise linearization as described by Heap et al (1997) is that it only approximates the non-linear shape variations because it is not using a priori knowledge on the organization of structures. However even though Mahmoodi et al (2000) did use the a priori knowledge, it was used only to initialize a model and not throughout the segmentation process. introduced the statistical model of topology to model the spatial relationships of anatomical structures.…”

Section: Introductionmentioning

confidence: 99%

“…A statistical model is a deformable model which in a compact way describes the information contained in a training dataset. These models were successfully applied to the segmentation of bony structures, e.g., vertebrae (Hill et al, 1996), spine (Smyth et al, 1997), knee joint (Cootes et al, 1998), hand (Mahmoodi et al, 2000), rib cage (van Ginneken and Haar Romeny, 2000) and hip/pelvis ).…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Anatomical Structures by Connected Statistical Models

Bukovec¹,

Likar²,

Pernuš³

2011

Image Anal Stereol

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This paper presents a framework for the segmentation of anatomical structures in medical imagery by connected statistical models. The framework is based on three types of models: first, generic models which operate directly on image intensities, second, connecting models that impose restrictions on the spatial relationship of generic models, and third, a supervising model that represents an arbitrary number of generic and connecting models. In this paper, the statistical model of appearance is used as the generic model, whiles the statistical model of topology, obtained by applying principal component analysis (PCA) on aligned pose and shape parameters of the generic model, is used as the connecting model. The performance of such connected statistical model is demonstrated on anterior-posterior (AP) X-ray images of the hips and pelvis and compared to the modelling by one and six unconnected generic models. The most accurate and robust results were obtained by two-level hierarchical modelling, wherein connected statistical models were used first, followed by unconnected statistical models.

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Skeletal growth estimation using radiographic image processing and analysis

Cited by 62 publications

References 16 publications

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Automated Human Bone Age Assessment using Image Processing Methods - Survey

On the Segmentation and Classification of Hand Radiographs

Segmentation of Anatomical Structures by Connected Statistical Models

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