The resolution integral: visual and computational approaches to characterizing ultrasound images

被引:21
|
作者
MacGillivray, T. J. [1 ]
Ellis, W. [2 ]
Pye, S. D. [2 ]
机构
[1] Univ Edinburgh, Western Gen Hosp, Wellcome Trust Clin Res Facil, Edinburgh EH4 2XU, Midlothian, Scotland
[2] Royal Infirm, NHS Lothian Univ Hosp Div, Dept Med Phys, Edinburgh EH16 4SA, Midlothian, Scotland
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2010年 / 55卷 / 17期
关键词
MEDICAL ULTRASOUND; QUALITY-ASSURANCE; EDGE-DETECTION; SPECKLE; PERFORMANCE; PHANTOMS;
D O I
10.1088/0031-9155/55/17/012
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The resolution integral is a figure of merit that characterizes ultrasound images in terms of the ratio of the penetration of an ultrasound beam in soft tissue to the ultrasound beam width. This concept has been implemented using a novel tissue mimicking test object (the Edinburgh pipe phantom) that comprises a series of anechoic cylinders of different diameters embedded in a block of tissue-mimicking material. The resolution integral is calculated by imaging each cylinder in turn and measuring the depth range over which it can be detected. We have carried out these measurements using two complementary approaches: by visual assessment and using a computational approach. Data were collected from 12 transducers used on 12 different models of ultrasound scanner of various makes, ages and clinical performance. Transducer centre frequencies were in the range of 3 to 7.5 MHz. The computational approach makes use of standard image processing techniques to detect and segment anechoic structures in images of the test object. This was optimized against visual assessment results for one of the transducers, and subsequently used to evaluate the resolution integral for the others. The values of the resolution integral ranged from 40 to 69 and computed values were within +/- 11% of the corresponding visual assessments. The repeatability of both approaches was +/- 2-3%. The computational approach functions well compared to visual assessment and adds to the overall robustness of resolution integral measurements by providing an objective assessment algorithm.
引用
收藏
页码:5067 / 5088
页数:22
相关论文
共 50 条
  • [21] Characterizing clinically relevant natural variants of GPCRs using computational approaches
    Sengupta, Durba
    Sonar, Krushna
    Joshi, Manali
    G PROTEIN-COUPLED RECEPTORS, 2ND EDITION, PT A, 2017, 142 : 187 - 204
  • [22] CHARACTERIZING EPISODES OF LUCIDITY IN DEMENTIA: OBSERVATIONAL AND APPLIED COMPUTATIONAL LINGUISTICS APPROACHES
    Bykovskyi, Andrea Gilmore
    Mueller, Kim
    Werner, Nicole
    Smith, Erica
    Block, Laura
    Benson, Clark
    INNOVATION IN AGING, 2021, 5 : 47 - 47
  • [23] Computational Techniques for Characterizing Cognition using EEG Data - New Approaches
    Nandagopal, Nanda
    Vijayalakshmi, R.
    Cocks, Bernie
    Dahal, Nabaraj
    Dasari, Naga
    Thilaga, M.
    Dharwez, Shamshu S.
    17TH INTERNATIONAL CONFERENCE IN KNOWLEDGE BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS - KES2013, 2013, 22 : 699 - 708
  • [24] Computational approaches to identifying and characterizing protein binding sites for ligand design
    Henrich, Stefan
    Salo-Ahen, Outi M. H.
    Huang, Bingding
    Rippmann, Friedrich
    Cruciani, Gabriele
    Wade, Rebecca C.
    JOURNAL OF MOLECULAR RECOGNITION, 2010, 23 (02) : 209 - 219
  • [25] Computational Reconstruction of Integral Imaging Based on Elemental Images Stitching
    Wang Yu
    Yang Jinxiao
    Liu Le
    Piao Yan
    ACTA OPTICA SINICA, 2019, 39 (11)
  • [26] Multi-resolution parallel integral projection for fast localization of a straight electrode in 3D ultrasound images
    Uhercik, Marian
    Kybic, Jan
    Liebgott, Herve
    Cachard, Christian
    2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, : 33 - +
  • [27] Super-Resolution Processing of Computational Reconstructed Images
    Wang, Yu
    Piao, Yan
    2010 IEEE 10TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS (ICSP2010), VOLS I-III, 2010, : 1033 - 1035
  • [28] Computational Evaluation Methods of Visual Complexity Perception for Images
    Guo X.-Y.
    Li W.-S.
    Qian Y.-H.
    Bai R.-Y.
    Jia C.-H.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2020, 48 (04): : 819 - 826
  • [29] Computational reconstruction technique in integral imaging with enhanced visual quality
    Inoue, Kotaro
    Cho, Byeongwoo
    Yun, Hui
    Cho, Myungjin
    THREE-DIMENSIONAL IMAGING, VISUALIZATION, AND DISPLAY 2018, 2018, 10666
  • [30] Computational Integral Imaging Reconstruction Technique with High Image Resolution
    Piao, Yan
    Wang, Yu
    Zang, Jingfeng
    2009 ASIA-PACIFIC CONFERENCE ON INFORMATION PROCESSING (APCIP 2009), VOL 2, PROCEEDINGS, 2009, : 160 - 163