Towards Medical Ultrasound Image Segmentation with Limited Prior Knowledge

Booth, Brian G.; Patel, Vaibhavi; Lou, Edmond; Le, Lawrence H.; Li, X.

doi:10.1109/dspws.2006.265472

Cited by 2 publications

(1 citation statement)

References 10 publications

(13 reference statements)

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“…Por isso, conhecimento a priori é considerado necessário para uma segmentação adequada de imagens de ultrassom. (Booth, et al, 2006).…”

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Formação e processamento de imagens de ultrassom

Lee¹

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Acoustic imaging allows the inspection and analysis of biological tissue and others materials in a non-destructive way, what is desired to improve the knowledge and to insure the quality about the analyzed object. This work was motivated by the lack of quantitative methods of analysis, usually based on qualitative diagnoses from experienced inspectors, what can vary, as well the lack of imaging systems where the pure RF signal of ultrasound could be acquired. Quantitative methods of analysis are highly desired when developing new piezoelectric materiais, biomedic phantom models and new diagnoses methods. It was developed in this work: an electrical-mechanic scanning system synchronized with the ultrasound acquisition device and image formation by different methods that allowed the development of an algorithm to prepare and to segment these images and different soft tissue phantom models. It was noticed that a statistic Wiener filter, even with no advantages for qualitative analysis of an image, improved the automatic segmentation of it.

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“…Por isso, conhecimento a priori é considerado necessário para uma segmentação adequada de imagens de ultrassom. (Booth, et al, 2006).…”

Section: Capítulounclassified

Formação e processamento de imagens de ultrassom

Lee¹

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A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

Gupta

Gosain

Kaushal

2010

J Clin Monit Comput

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Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.

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Towards Medical Ultrasound Image Segmentation with Limited Prior Knowledge

Cited by 2 publications

References 10 publications

Formação e processamento de imagens de ultrassom

Formação e processamento de imagens de ultrassom

A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

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