Unsupervised image classification of medical ultrasound data by multiresolution elastic registration

Aschkenasy, Schlomo V.; Jansen, Christian; Osterwalder, Remo; Linka, André; Unser, Michaël; Märsch, Stephan; Hunziker, Patrick

doi:10.1016/j.ultrasmedbio.2006.03.010

Cited by 33 publications

(19 citation statements)

References 11 publications

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“…Since no a priori domain-specific knowledge is exploited in their computation, they are primarily used with vector distance-based similarity analyses 14,40,55,57 in high-dimensional feature vector spaces or with statistical classifiers. 21,24,32,40,55,57,58 However, in a few cases, these general descriptors have been used with elastic deformation-based 59 and graph matchingbased 55 similarity analyses. In the former case, a descriptor is continuously deformed into another so that the deformation energy serves as a similarity measure between the two descriptors.…”

Section: Image Descriptorsmentioning

confidence: 99%

“…Thus, the similarity between two shapes is defined up to a rigid body transformation (translation + rotation) and an isotropic scaling. It is possible to extend this notion of similarity to allow nonrigid deformations of the shapes such as via elastic matching methods as in 59,61 . In fact, approaches based on continuous (and even diffeomorphic) mappings are becoming increasingly popular in the medical domain.…”

Section: Similarity Measuresmentioning

confidence: 99%

“…Yet, some systems preferred to design segmentation-free image descriptors to avoid the complications due to imperfect segmentation. 14, 15,24,36,38,56,59 Relevance Feedback A promising technique to fill the semantic gap in the medical CBIR systems is to adopt an expertin-the-loop approach. This refers to integrating the physician's high-level expert knowledge into the retrieval process by acquiring his/her relevance judgments regarding a set of initial retrieval results.…”

Section: Segmentationmentioning

confidence: 99%

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Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Section: Image Descriptorsmentioning

confidence: 99%

Section: Similarity Measuresmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

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Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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“…In older versions, Polaroid cameras were used. Today, algorithms have been proposed to manage and classify (in an unsupervised fashion) the vast amount of noisy images found in the medical unit [112] [113] [114] as well as the use of fuzzy logic [115].…”

Section: Aids For Measurement and Registermentioning

confidence: 99%

Application of ultrasound in medicine part ii: the ultrasonic transducer and its associated electronics

2013

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This paper introduces the reader to a review and analysis of various ultrasound (us) applications in the medical field. First, the transducer is shown, along with a diagram of the basic electronics that it uses to generate and receive signals that allow the reconstruction of images, for medical purposes. Also, us practical uses in medical therapy is shown. This subject is addressed by combining the physical principles involved in the us application, with the implemented technology and the modes of operation for non-invasive studies of pathologies related to brain, heart, and eyes such as injuries, tumors and hematomas. The methodology used in this article also suggests an analysis method for the us scientific and technological research principles combined for their implementation in medical applications.Keywords: ultrasonic transducer, piezoelectric, transmitter, receiver, converter, modes ResumenEn este trabajo se presenta al lector una revisión y análisis de diferentes aplicaciones del ultrasonido (us) en el campo médico. Inicialmente se muestra el transductor y un esquema de su electrónica básica asociada usada para emitir y adquirir las señales que permiten la reconstrucción de las imágenes, para efectos médicos y también la aplicación del us para terapia; la temática se aborda conjugando los principios físicos asociados a la aplicación del us con la tecnología implementada y sus modos de operación, para estudios no invasivos de patologías relacionadas con el cerebro, el corazón, los ojos y lesiones tales como tumores y hematomas. La metodología implementada en este artículo sugiere también un método de análisis de los principios científicos del us e investigación tecnológica asociadas para su concreción en las aplicaciones médicas.

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“…Zhou et al recognized the A4C image by texture analysis [4]. Schlomo et al utilized multi-scale elastic registration to match the unknown sample image onto known templates to distinguish the A4C image [5]. All these papers focus on the A4C image detection from large amounts of two-dimensional echocardiographic images.…”

Section: Introductionmentioning

confidence: 99%

Automatic acquisition of the four-chamber view for 3D echocardiography

Liu¹,

Yang²

2008

IEICE Electron. Express

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Abstract:To assist the diagnosis and analysis of heart disease, we introduce a template matching based method to detect the apical four-chamber (A4C) image plane in the three-dimensional echocardiographic (3DE) dataset. Firstly, a typical A4C image is selected as the template. Secondly, a series of cross-sections are extracted from a 3DE image. Finally, by a histogram-based retrieval, the cross-section most similar to the template is retrieved as the A4C image in this 3DE image. With low computational complexity and easy implementation, this method can detect the A4C image plane in 3DE images of different subjects by only one template image. Keywords: real-time three-dimensional echocardiography, template matching, image retrieval Classification: Science and engineering for electronics References[1] J. A. Panza, "Real-time three-dimensional echocardiography: an overview," Int.

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Unsupervised image classification of medical ultrasound data by multiresolution elastic registration

Cited by 33 publications

References 11 publications

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Application of ultrasound in medicine part ii: the ultrasonic transducer and its associated electronics

Automatic acquisition of the four-chamber view for 3D echocardiography

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