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A comparative study of ultrasound image segmentation algorithms for segmenting kidney tumors

Published: 26 October 2011 Publication History

Abstract

In this paper we introduce an ultrasound image segmentation evaluation framework for kidney tumor. Ultrasound image segmentation algorithms can be divided into edge based, region based, texture based, active contour and model base technique. We tested the performance of algorithms in each category using a kidney phantom and kidney cyst ultrasound image. We found that the algorithms we implemented are more suitable for relatively homogeneous kidney tumors. For more heterogeneous tumors we should use more complicated segmentation techniques and some of these advanced techniques are discussed in this paper.

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Cited By

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  • (2022)Kidney Tumor Segmentation Based on FR2PAttU-Net ModelFrontiers in Oncology10.3389/fonc.2022.85328112Online publication date: 17-Mar-2022
  • (2022)Artificial intelligence optimized image segmentation techniques for renal cyst detectionJournal of Medical Engineering & Technology10.1080/03091902.2022.208088246:5(415-423)Online publication date: 31-May-2022
  • (2022)Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A ReviewPervasive Computing and Social Networking10.1007/978-981-16-5640-8_15(183-199)Online publication date: 1-Jan-2022
  • Show More Cited By

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cover image ACM Other conferences
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
October 2011
949 pages
ISBN:9781450309134
DOI:10.1145/2093698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Universitat Pompeu Fabra
  • IEEE
  • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
  • River Publishers: River Publishers
  • CTTC: Technological Center for Telecommunications of Catalonia
  • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

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Author Tags

  1. algorithm
  2. kidney tumor
  3. segmentation
  4. ultrasound image

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ISABEL '11
Sponsor:
  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

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Cited By

View all
  • (2022)Kidney Tumor Segmentation Based on FR2PAttU-Net ModelFrontiers in Oncology10.3389/fonc.2022.85328112Online publication date: 17-Mar-2022
  • (2022)Artificial intelligence optimized image segmentation techniques for renal cyst detectionJournal of Medical Engineering & Technology10.1080/03091902.2022.208088246:5(415-423)Online publication date: 31-May-2022
  • (2022)Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A ReviewPervasive Computing and Social Networking10.1007/978-981-16-5640-8_15(183-199)Online publication date: 1-Jan-2022
  • (2020)A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization ApproachArchives of Computational Methods in Engineering10.1007/s11831-020-09469-3Online publication date: 8-Aug-2020
  • (2017)Phase based distance regularized level set for the segmentation of ultrasound kidney imagesPattern Recognition Letters10.1016/j.patrec.2016.12.00286:C(9-17)Online publication date: 15-Jan-2017
  • (undefined)Kidney and Tumor Segmentation using U-Net Deep Learning ModelSSRN Electronic Journal10.2139/ssrn.3527410

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