Literature DB >> 24212021

Detection and measurement of fetal abdominal contour in ultrasound images via local phase information and iterative randomized Hough transform.

Weiming Wang1, Jing Qin, Lei Zhu, Dong Ni, Yim-Pan Chui, Pheng-Ann Heng.   

Abstract

Due to the characteristic artifacts of ultrasound images, e.g., speckle noise, shadows and intensity inhomogeneity, traditional intensity-based methods usually have limited success on the segmentation of fetal abdominal contour. This paper presents a novel approach to detect and measure the abdominal contour from fetal ultrasound images in two steps. First, a local phase-based measure called multiscale feature asymmetry (MSFA) is de ned from the monogenic signal to detect the boundaries of fetal abdomen. The MSFA measure is intensity invariant and provides an absolute measurement for the signi cance of features in the image. Second, in order to detect the ellipse that ts to the abdominal contour, the iterative randomized Hough transform is employed to exclude the interferences of the inner boundaries, after which the detected ellipse gradually converges to the outer boundaries of the abdomen. Experimental results in clinical ultrasound images demonstrate the high agreement between our approach and manual approach on the measurement of abdominal circumference (mean sign difference is 0.42% and correlation coef cient is 0.9973), which indicates that the proposed approach can be used as a reliable and accurate tool for obstetrical care and diagnosis.

Entities:  

Keywords:  Ultrasound images; fetal abdominal contour; iterative randomized Hough transform; multiscale feature asymmetry

Mesh:

Year:  2014        PMID: 24212021     DOI: 10.3233/BME-130928

Source DB:  PubMed          Journal:  Biomed Mater Eng        ISSN: 0959-2989            Impact factor:   1.300


  2 in total

1.  Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification.

Authors:  Umar Manzoor; Samia Nefti; Milella Ferdinando
Journal:  J Med Syst       Date:  2016-09-01       Impact factor: 4.460

Review 2.  Artificial Intelligence in Prenatal Ultrasound Diagnosis.

Authors:  Fujiao He; Yaqin Wang; Yun Xiu; Yixin Zhang; Lizhu Chen
Journal:  Front Med (Lausanne)       Date:  2021-12-16
  2 in total

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