Abstract
This paper presents a new analytical method for fetal biometric measurements. The proposed approach is used to validate doctor’s biometric measurements to detect the malformation of the head. Mainly, biparietal diameter, occipito-frontal diameter, and head circumference are the well-known clinical measurements used to detect the fetal head. Here, the ultrasound images (US) of the gestational age (GA = 22 weeks) are employed using a discriminative set of features. The proposed system consists of the following steps: First, the ultrasound fetal image is passed through the Contourlet preprocessing algorithm to reduce the speckle noise. The resulting image falls to hysteresis thresholding process to preserve the dominant head features. Then, log Gabor features are extracted from the US image to determine the biometric head measurements. The obtained measurements are compared to Expert results using normal and abnormal data sets. The proposed method has been tested on 50 normal and 10 neurological abnormal fetal head US sequences and it reach 98% of accuracy. The main advantages of the new proposed biometric measurement technique versus the existing procedures are: (1) automatic measure of the fetal head displayed on the computer screen of the expert machine without manual intervention; (2) the approach should be implemented and the biometric measure can be extracted automatically from the clinical US image.
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References
Chen Y, Yin R, Flynn P, Broschat S (2003) Aggressive region growing for speckle reduction in ultrasound images. Pattern Recogn Lett 24(4):677–691
Do MN, Vetterli M (2002) Contourlets: a directional multiresolution image representation. In: 2002 international conference on image processing, vol 1, pp. I–I. IEEE
Do MN, Vetterli M (2005) The Contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106
Elamvazuthi I, Zain MLBM, Begam KM (2013) Despeckling of ultrasound images of bone fracture using multiple filtering algorithms. Math Comput Model 57(1):152–168
Fathima S, Rueda S, Papageorghiou A, Noble JA (2011) A novel local-phase method of automatic atlas construction in fetal ultrasound. In: SPIE medical imaging, p 79621A
Foi A, Maggioni M, Pepe A, Tohka J (2012) Head contour extraction from the fetal ultrasound images by difference of gaussians revolved along elliptical paths. In: Proceedings of challenge US: biometric measurements from fetal ultrasound images, ISBI, pp 1–3
Garg A, Goal J, Malik S, Choudhary K (2011) De-speckling of medical ultrasound images using Wiener filter and wavelet transform. Int J Electron Commun Technol 2:21–24
Huang QH, Zheng YP, Lu MH, Chi ZR (2005) Development of a portable 3D ultrasound imaging system for musculoskeletal tissues. Ultrasonics 43(3):153–163
Karthikeyan K, Chandrasekar C (2011) Speckle noise reduction of medical ultrasound images using bayesshrink wavelet threshold. Int J Comput Appl 22(9):8–14
Kovesi P (1996) Invariant measures of image features from phase information. University of Western Australia
Kovesi P (1999) Image features from phase congruency. Videre J Comput Vis Res 1(3):1–26
Kumar S, Sarfaraz M, Ahmad MK (2017) Denoising method based on wavelet coefficients via diffusion equation. Iran J Sci Technology Trans A Sci 1–6. https://doi.org/10.1007/s40995-017-0228-7
Latifoğ Lu F (2013) A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Comput Methods Progr Biomed 111(3):561–569
Loizou CP, Theofanous C, Pantziaris M, Kasparis T (2014) Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery. Comput Methods Progr Biomed 114(1):109–124
Loughna P, Chitty L, Evans T, Chudleigh T (2009) Fetal size and dating: charts recommended for clinical obstetric practice. Ultrasound 17(3):160–166
Lu W, Tan J, Floyd R (2005) Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform. Ultrasound Med Biol 31(7):929–936
Michailovich OV, Tannenbaum A (2006) Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 53(1):64–78
Mohanapreethi A, Srinivasaraghavan V (2014) Performance evaluation of various filtering techniques for speckle suppression in ultrasound images. Int J Res Advent Technol 2(4)
Mousavi Z, Lakestani M, Razzaghi M (2017) Combined shearlet shrinkage and total variation minimization for image denoising. Iran J Sci Technol Trans A Sci 1–7. https://doi.org/10.1007/s40995-017-0327-5
Namburete AI, Noble JA (2013) Fetal cranial segmentation. In: 2D ultrasound images using shape properties of pixel clusters, IEEE 10th international symposium on biomedical imaging (ISBI), pp 720–723
Namburete AI, Rahmatullah B, Noble JA (2013) Nakagami-based Adaboost learning framework for detection of anatomical landmarks in 2D fetal neurosonograms. Ann BMVA 2:1–16
Namburete AI, Yaqub M, Kemp B, Papageorghiou AT, Noble JA (2014) Predicting fetal neuro developmental age from ultrasound images. In: International conference on medical image computing and computer-assisted intervention, pp 260–267
Namburete AI, Stebbing RV, Kemp B, Yaqub M, Papageorghiou AT, Noble JA (2015) Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med Image Anal 21(1):72–86
Ni D, Yang X, Chen X, Chin CT, Chen S, Heng PA, Wang T (2014) Standard plane localization in ultrasound by radial component model and selective search. Ultrasound Med Biol 40(11):2728–2742
Ponomarev GV, Gelfand MS, Kazanov MD (2012) A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. In: Proceedings of challenge US: biometric measurements from fetal ultrasound images, ISBI, pp 17–19
Pramanik M, Gupta M, Krishnan KB (2013) Enhancing reproducibility of ultrasonic measurements by new users. In: International society for optics and photonics SPIE medical imaging, pp 86730–86730
Rackham TM, Rueda S, Knight CL, Noble JA (2013) Ultrasound image segmentation using feature asymmetry and shape guided live wire. In: SPIE medical imaging, p 86690P
Ragesh NK, Anil AR, Rajesh R (2002) Digital image denoising in medical ultrasound images: a survey. In: ICGST AIML-11 conference, Dubai, UAE, vol 12, p 14
Rueda S, Knight CL, Papageorghiou A, Noble JA (2011) Local phase-based fuzzy connectedness segmentation of ultrasound images. In: MIUA, pp 331–336
Rueda S, Knight CL, Papageorghiou AT, Noble JA (2013). Oriented feature-based coupled ellipse fitting for soft tissue quantification in ultrasound images. In: Biomedical imaging (ISBI), pp 1014–1017
Sudha S, Suresh GR, Sukanesh R (2009) Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. Int J Comput Theory Eng 1(1):7
Tay PC, Acton ST, Hossack JA (2011) A wavelet thresholding method to reduce ultrasound artifacts. Comput Med Imaging Gr 35(1):42–50
Thakur A, Anand RS (2005) Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction. Digit Signal Process 15(5):455–465
Weickert J, Scharr H (2002) A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. J Vis Commun Image Represent 13(1–2):103–118
Wu K, Shu H, Dillenseger JL (2014) Region and boundary feature estimation on ultrasound images using moment invariants. Comput Methods Progr Biomed 113(2):446–455
Xiao CY, Su Z, Chen YZ (2004) A diffusion stick method for speckle suppression in ultrasonic images. Pattern Recogn Lett 25(16):1867–1877
Acknowledgements
Authors wish to thank the team of the Department of Maternity at Charles Nicole Hospital, Tunis, Tunisia, for their permission to employ fetal ultrasound data. In addition, authors wish to thank Dr. Haykel Kchok, angiologist at Menzeh, Tunis, for his useful and relevant advices during the extraction of biometric measurements.
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Sahli, H., Zaafouri, A., Ben Slama, A. et al. Analytic Approach for Fetal Head Biometric Measurements Based on Log Gabor Features. Iran J Sci Technol Trans Sci 43, 1049–1057 (2019). https://doi.org/10.1007/s40995-018-0523-y
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DOI: https://doi.org/10.1007/s40995-018-0523-y