Ultrasonic Attenuation Imaging — Comparison of Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

According to the American Cancer Society, breast cancer is the second most common form of cancer in american women after skin cancer and the second major cause of cancerrelated death after lung cancer. Still mammography is the imaging option guaranteeing the best diagnostic sensitivity but it causes high cost, is subjecting women to ionizing radiation, and is not readily available and not advisable to screen women on a very dense regular basis. Ultrasonic imaging, however is readily available in any gynecologist’s clinic but still lags behind in sensitivity. Ultrasonic attenuation imaging, an imaging modality providing information on the local absorption property of the propagation medium, the various breast tissues, can help the clinician in her diagnosis by providing a color-coded overlay over the morphology that can be imaged using standard B-mode. We discuss and compare the results obtained by two attenuation estimation algorithms, the Method-of-Moments and the Spectrallog-Difference method operating on k-wave-simulated ultrasonic B-mode volume data. We also elaborate on the numerical complexity and the cost in terms of processing power necessary for both algorithms. The results show comparable performance with the MoM resulting in a smaller processor load as compared to the SLD technique.
Original languageEnglish
Title of host publicationProceedings of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Editors IEEE
Number of pages6
ISBN (Electronic)9781665412629
DOIs
Publication statusPublished - 07 Oct 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fields of science

  • 202012 Electrical measurement technology
  • 202036 Sensor systems
  • 102003 Image processing
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202037 Signal processing

JKU Focus areas

  • Digital Transformation

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