Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models

Zouhar, Alexander; Fang, Tong; Ünal, Gözde; Slabaugh, Greg; Xie, Hui; McBagonluri, Fred

doi:10.1109/3dpvt.2006.29

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…Similar to Zouhar et al [4], we use a specifically adapted version of an iterative closest point (ICP) algorithm to register ear impressions. In contrast to them, we employ a version without previously detected features, since we want to be independent of them.…”

Section: Robust Alignment Of Ear Impressionsmentioning

confidence: 99%

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Clustering-Based Detection of Anatomical Features on Organic Shapes

Sickel

Bubnik

2011

Bildverarbeitung Für Die Medizin 2011

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Abstract. Robust and accurate automatic detection of anatomical features on organic shapes is a challenging task. Despite a rough similarity, each shape is unique. To cope with this variety, we propose a novel clustering-based feature detection scheme. The scheme can be used as a standalone feature detection scheme or it can provide meaningful starting points for surface analyzing-based detection algorithms. The scheme includes the identification of a representative set of shapes and the usage of a specialized iterative closest point algorithm for the registration of shapes, which is followed by the projection of the features using the transformation matrix of the registration. Evaluation is based on a large set of expert annotated shapes and showed superior performance compared to state-of-the-art surface analyzing methods. Accuracy increased of 32 % and detection of all features is ensured.

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Section: Robust Alignment Of Ear Impressionsmentioning

confidence: 99%

“…Zouhar et al focused on the detection of anatomical features to guide a fast registration of 3-D ear impressions and also for automation purposes [4,5]. Baloch et al worked on the detection of a canonical ear signature to capture the structure of an ear impression [6].…”

Section: Introductionmentioning

confidence: 99%

Clustering-Based Detection of Anatomical Features on Organic Shapes

Sickel

Bubnik

2011

Bildverarbeitung Für Die Medizin 2011

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“…Our motivation is also tied to not having to resort to feature extraction, which often is more prone to errors with variations in the shape geometry. For instance, problems with aperture feature detection in ear impressions, were discussed in a feature-based alignment of ear canal models work of Zouhar et al [27].…”

Section: Our Contributionmentioning

confidence: 99%

“…Based on a variant of ICP, the rigid alignment of all the undetailed shapes were carried out by the method presented in [27] before they were converted to a voxelized signed distance transform representation.…”

Section: Biased Shape Registrationmentioning

confidence: 99%

Generating shapes by analogies: An application to hearing aid design

Ünal¹,

Nain²,

Slabaugh³

et al. 2011

Computer-Aided Design

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link Abstract3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient's anatomy.In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to estimation of customized hearing aid devices.

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“…It shall be constrained in such a way that a modification of one part has no confounding effect on other parts. In this work, we use the algorithm explained in [16] to automatically decompose a surface mesh of an ear-mold into canal part and external ear part. The decomposition is defined by the aperture contour, which is shown in Fig.…”

Section: Our Contributionmentioning

confidence: 99%

Freeform shape clustering for customized design automation

Zouhar

Baloch

Azernikov

et al. 2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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Automation can provide significant performance improvements in digital manufacturing systems that customize shapes of implants and prosthetic devices to the anatomy of a patient. The challenge, however, lies in the ability of an automatic solution to adapt to anatomical variations of a given object category. This paper presents a hierarchical framework that generalizes the digital design of anatomical surface models in terms of a small number of prototypes. The latter are derived from the local shape information of constituent parts via shape matching and clustering and then associated with one operation that dictates how a shape undergoes modification. We demonstrate the proposed technique through application to typical hearing aid design operations with promising results.

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Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models

Cited by 6 publications

References 11 publications

Clustering-Based Detection of Anatomical Features on Organic Shapes

Clustering-Based Detection of Anatomical Features on Organic Shapes

Generating shapes by analogies: An application to hearing aid design

Freeform shape clustering for customized design automation

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