Automatic Detection of Anatomical Features on 3D Ear Impressions for Canonical Representation

Baloch, Sajjad; Melkisetoglu, Rupen; Flöry, Simon; Azernikov, Sergei; Slabaugh, Greg; Zouhar, Alexander; Fang, Tong

doi:10.1007/978-3-642-15711-0_69

Cited by 8 publications

(6 citation statements)

References 4 publications

(7 reference statements)

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“…Baloch et al proposed that a shape descriptor for 3D ear impressions, derived from a comprehensive set of anatomical features. 17 Motivated by hearing aid (HA) manufacturing, the selection of the anatomical features is carried out according to their uniqueness and importance in HA design.…”

Section: Figurementioning

confidence: 99%

The Shape of the External Human Ear: A Geometric Morphometric Study

Ocakoğlu¹,

Ozdemir²,

Ercan³

et al. 2013

Turkiye Klinikleri J Med Sci

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A AB BS ST TR RA AC CT T O Ob bj je ec ct ti iv ve e: : The aim of this study was to identify the shape variability of the external ear. Variability in the shape of the ear was investigated based on the type of ear, gender and facial side in terms of the shape of the helix and antihelix and the concha. M Ma at te er ri ia al l a an nd d M Me et th ho od ds s: : A landmark-based geometric morphometric technique was used to analyze the external shape of the ear. The study included 330 (177 females and 153 males) voluntary young adults aged between 18 and 24 years. All data were obtained from standardized digital photographic images taken from the right and left sides of the face. The data from the landmark coordinates were analyzed using statistical shape analysis. R Re es su ul lt ts s: : Earlobes were classified according to the angle between the earlobe and the cheek. In accordance with this classification in our study, ear type was classified as type I (tapering-acute angle), type II (square-right angle) and type III (pendulous-obtuse angle) according to the appearance of the lobule. There were significant differences between the gender groups for right and left side separately regarding the shapes of the helix and the shape of the antihelix and the concha in type I ears. There were significant differences between type I and type II ears for right and left side separately regarding the shapes of the helix and the shape of the antihelix and the concha in females. C Co on nc cl lu us si io on n: : The present study serves as a guide to future clinical studies by demonstrating localized variations in the components of the ear that constitute the overall shape of the ear. K Ke ey y W Wo or rd ds s: : Ear; shape; morphometry; statistical shape analysis; geometric morphometrics Ö ÖZ ZE ET T A Am ma aç ç: : Bu çalışmanın amacı, dış kulağa ait şekil değişkenliğini tanımlamaktır. Kulağın şek-lindeki değişkenlik kulak tipi, cinsiyet ve tarafa göre heliks ile antiheliks ve konkanın şekilleri açısından incelenmiştir. G Ge er re eç ç v ve e Y Yö ön nt te em ml le er r: : Çalışmada, dış kulağın şeklini analiz etmek için nirengi noktası tabanlı geometrik morfometrik bir yöntem kullanılmıştır. Çalışmaya yaşları 18-24 arasında değişen 330 (177 kadın, 153 erkek) gönüllü genç erişkin dâhil edilmiştir. Tüm veriler yüzün sağ ve sol tarafından çekilen standartlaştırılmış dijital fotografik görüntülerden elde edilmiştir. Mirengi noktası koordinatlarından elde edilen veriler istatistiksel şekil analizi kullanıla-rak analiz edilmiştir. B Bu ul lg gu ul la ar r: : Kulak lobu, kulak lobu ile çene arasındaki açı dikkate alınarak sınıflanmıştır. Buna göre çalışmamızda kulak tipi, tip I (sivri-dar açılı), tip II (kare-dik açılı) ve tip III (pendülöz-geniş açılı) olarak sınıflanmıştır. Tip I kulak içinde yapılan karşılaştırmalarda, erkekler ve kadınlar arasında hem sağ kulağın, hem de sol kulağın heliks ile antiheliks ve konka şekillerine göre anlamlı farklılık bulunmuştur. Kadınlarda, tip I ve tip II kulak tipleri arasında hem sağ kulağın,...

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Section: Figurementioning

confidence: 99%

The Shape of the External Human Ear: A Geometric Morphometric Study

Ocakoğlu¹,

Ozdemir²,

Ercan³

et al. 2013

Turkiye Klinikleri J Med Sci

View full text Add to dashboard Cite

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“…1. We compared our CBD scheme with a surfaceanalysis-based (SBD) one developed by Baloch et al [6]. The results presented in Tab.…”

Section: Resultsmentioning

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]. So far, the proposed algorithms for feature detection on ear impressions were solely based on the analysis of surface properties, like peaks, depressions, concavities, ridges and bumps.…”

Section: Introductionmentioning

confidence: 99%

“…So far, the proposed algorithms for feature detection on ear impressions were solely based on the analysis of surface properties, like peaks, depressions, concavities, ridges and bumps. Hence, the result is sensitive to the quality of the acquired ear impression mesh and can fail in case of unusual ear impressions [6]. Problematic cases are characterized by containing large amounts of excess material, very short ear canals, holes all over the impression and a very jagged opening contour at the bottom of the impression.…”

Section: Introductionmentioning

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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“…3 shows a selection of the detected features. A detailed description of the features and used algorithms is given in [21].…”

Section: Expert System Frameworkmentioning

confidence: 99%

Genetic programming for Expert Systems

Sickel

Hornegger²

2010

IEEE Congress on Evolutionary Computation

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Abstract-Genetic programming is the usage of the paradigm of survival of the fittest in scientific computing. It is applied to evolve solutions to problems where dependencies between multiple input factors are unknown. In this paper we propose and evaluate the application of a specifically adapted genetic programming framework to optimize the rule base of an expert system. The expert system controls a computer-aideddesign software and targets the automation of a manufacturing process.The used steady state genetic programming framework introduces some variations on the selection and evolution operators normally used in genetic programming. In particular: size enforcing mutation, dynamic fitness calculation and size constraint ranking. The genetic programming system is evaluated with real data and led to an improved expert system performance of about 22 percent.

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Automatic Detection of Anatomical Features on 3D Ear Impressions for Canonical Representation

Cited by 8 publications

References 4 publications

The Shape of the External Human Ear: A Geometric Morphometric Study

The Shape of the External Human Ear: A Geometric Morphometric Study

Clustering-Based Detection of Anatomical Features on Organic Shapes

Genetic programming for Expert Systems

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