Automatic fetal measurements for low-cost settings by usingLocal Phase Bone detection

Benjamin Amoah 1,2, Evelyn Arthur Anto 1,2, Alessandro Crimi 1,2

Abstract— The estimation of gestational age isdone mostly by measurements of fetal anatomicalstructures such as the head and femur. These mea-surement are also used in diagnosis and growthassessment. Manual measurements is operator de-pendent and hence subject to variability.

By the proposed method, fetal femurs are au-tomatically measured and gestational ages, hencedelivery dates, are predicted. The initial step is thedetection of biometric elements in fetal ultrasoundscans through local image phase features obtainedby using Gabor filter banks which detect structuresbased on the frequency nature of the soft tissue/boneinterfaces. The second step is a regression method torelate the obtained features to the gestational age.

The lengths of 20 femurs are analyzed in ourexperiments. The resulting measurements are con-sistent with manual measurements obtained by twoexpert technicians. The method is fully automaticand can replace the manual approach to measuringfemur of fetuses in US images.

I. INTRODUCTION

Ultrasound (US) imaging is preferred in ob-stetrics because it is non-invasive, more econom-ical, safe (since it does not involve any ionizingradiation) and the images can be produced atvideo rate, enabling the observation of the dynamicbehavior of structures over time. The portabilityof ultrasound scan machines gives US imagingan extra advantage. Obstetricians are concernedabout qualitative measurements such as the loca-tion, orientation and movement of structures ofinterest and quantitative measurements of lengths,areas and volumes of these structures. Keen amongsuch measurements are the head circumference,femur length, bi-parietal diameter and the occipitalfrontal diameter. The goals of such measurementsare effective diagnosis and assessment of growthof foetus. To take such measurements, the contoursof anatomical structures are extracted (segmented).Manual extraction (which is the current approachto measuring contours of foetal anatomies) is te-dious, time consuming and subject to variability

This work was supported by ETH-Zurich, Switzerland andAfrican Institute for Mathematical Sciences, Ghana

1 Swiss Federal of Technology (ETH-Zurich)2 African Institute for Mathematical Sciences, Ghana

in human operators. Variability in measurementobtained by different operators and even the sameoperator at different attempts make the results notreproducible and hence unreliable.

Little attempts have been made to automatethe extraction of contours in ultrasound imagesalthough a lot have been done with other imagingmodalities. This is, arguable, due to the fact thatultrasound images are of very poor quality as a re-sult of speckle noise inherent in ultrasound images[1], [5]. General purpose segmentation approacheshave not been able to deal with the problem.Previous works involved the use discriminativeconstrained probabilistic boosting tree classifiers tosegment structures [3] and iterative approaches tosegment fetal structures by Maximum-Likelihoodestimation [2]. These techniques aim at detectingin US images, features such as the parietal di-ameter (BPD), occipital-frontal diameter (OFD),head circumference (HC) and femur length (FL)[4]. Recently neuro-developmental maturation of afetus based on 3D ultrasound has been introducedbecause neurological age-discriminating anatomiessuch as the Sylvian fissure, cingulate and callosalsulci are more indicative than traditional features[6].

However, all these methods have been testedmainly in high-end devices which even if con-sidered low-cost in high income countries, theymight not be accessible in low-income countries.With the goal of improving prenatal care manage-ment in rural areas in Ghana, a pilot project hasbeen carried out in some communities betweenthe cities of Accra and Cape-Coast [11]. Thisinvolved community health workers together withUS technicians using low-cost portable ultrasoundmachine as depicted in Fig. 1.

In the following section we report our method,which is based on a combination of Gabor filtering,thresholding and structural analysis to automati-cally segment the femur in US images and to pre-dict gestational age or delivery date. The remainderof the paper reports the experiments, discussionand conclusion.

Fig. 1: Example of ultrasound acquisition in low-cost settings carried out within the project, in ahome of a rural community and not in hospitals orclinics.

II. METHODS

The method is based on image phase informa-tion used for processing US images of hard tissuessuch as bones. The phase information is derived bya bank of Gabor filters (i.e. kernels of Gaussianfunctions modulated by sinusoidal plane waves)applied to a given ultrasound scan. A complexGabor filter in 2D is given by (1).

g(x,y,λ ,φ ,σ ,γ) = exp(− x′2+γ2y′2

2σ2

)exp(

2i(π

x′λ+φ)),(1)

where {λ ,θ ,φ ,σ ,γ} = {wavelength, angle be-tween normal and parallel stripes of the Gaborfunction, phase offset, standard deviation, spatialaspect ratio} and

(x′, y′) = (xcosθ + ysinθ ,−xsinθ + ycosθ).

For simplicity Equation (1) will be written asg(.). Let Mem(x,y) = real(F−1(g(.)) and Mom(x) =imag(F1(g(.)) denote the even and odd Log-Gaborfilters at a scale m, with F1 being the inverseFourier transform operation. If the original imageis I(x,y), then the response will be

em(x,y) = I(x,y)?Mem(x,y), (2)

andom(x,y) = I(x,y)?Mom(x,y). (3)

Hacihaliloglu et al. [7], [8] argued that a ridgedetector of bone surface is given by a detectorof major axis of symmetry of these transforms.Therefore a ridge bone detector is given by takingthe difference of these responses over a number ofscales as the measure of phase symmetry (PS) [9],using different orientation r:

PS(x) =∑r ∑m ||erm(x,y)−|orm(x,y)||−T

∑r ∑m√

erm(x,y)2 +orm(x,y)2 + ε, (4)

where ε is a small number to avoid division by zeroand T is a noise threshold. These are dependent onthe specific US machine and found empirically.

The PS image has a maximum at the boneboundary and hence can be used to identifythe edge of the bone. In our experiments,we used the parameter values {λ ,φ ,σ ,γ} ={4.0,11.0,1.0,0.0} which remained constant forall the testing images. Gabor filters with r = 10orientations given by θ = nπ

9 , n ∈ {0,1,2, . . . ,9}were used to filter each image. The results ofthe PS image were then binarized using an au-tomatic threshold method [10], and then, a dila-tion followed by an erosion with the structuringelement for the 4-neighborhood are applied. Theused size of the morphological operators was 5.Although this size was not optimized through theused dataset, it was noticed that an operator largerthan 10 was compromising the segmentation. Theparameters were obtained base on experimentalresults.

A. Femur length

Before applying the regression model to predictthe gestational age, small structures are removed.This was done by using prior knowledge that thefemur is the longest of all structures in a resultingimage. The length of each femur was measuredby the length of the rectangle with minimum areathat encloses all pixels which belong to the femurcontour. The rectangle co-ordinates were computedautomatically [16].

B. Prediction of gestational age

Once the FL is computed, the gestational ageis computed using the Hadlock regression for-mula used for singleton pregnancies obtained frommixed population [12], [13]:

GA = 1.863+6.280FL−0.211FL2 (5)

C. Data

The US scans used were obtained from 20 preg-nant women at different stages of pregnancy by us-ing a curvilinear transducer array with 4.5MHz anda B-Mode US machine (Mindray, Shenzen, China).All women gave written consent and the studyhas been carried out according to the Helsinkideclaration. These images were gray-scale imageswith resolution 800 × 600 pixels which wereacquired using different image gain for a better

view, removing labels and menu from the resultingimages. Image acquisition was not carried out inhospitals or clinics, but in private homes of preg-nant women within rural communities in Ghana.

III. EXPERIMENTS AND EVALUATION

The proposed approach was tested on n= 20 dif-ferent ultrasound images of femurs obtained fromdifferent pregnant women. An example of resultsis depicted in Fig. 2, where the original image,the resulting image and the predicted gestationalage as the final output. A comparison between anUS scan processed using the Canny filter edgedetector and the PS image, before the selectionof the longest segment, is depicted respectively inFig.3 and Fig.4.

Fig. 3: Example of ultrasound scan processed usingCanny filter edge detector.

Fig. 4: Example result of the PS image before theselection of the longest edge as a femur.

To assess the accuracy of our approach, a kappa-statistic (κ) [17] is calculated for each of the 20segmented images. A careful manual segmenta-tion of each image is used as the ground truthsegmentation. The κ-values obtained (as shownin Table I) ranged has mean (±SD) being 0.74

(±0.09), which signifies substantial agreement be-tween the manual annotations and our automaticsegmentations. A Python script was written toenable two experts manually measure the lengthof the femurs in order to evaluate our automaticmeasurements. In Table I, E1, E2, and A arerespectively the measurements obtained by the firstexpert technician, second expert technician andour automatic measurements. E, which representsthe actual manual measurement of the femur, isthe average of E1 and E2; d1 = (E1 − E2)

2 andd2 = (E−A)2. The mean of d2 is 0.03. A pairedt-test (two-tailed p-value equals 0.1165) shows adifference is not statistically significant, thus, themeasurements by the our approach are at leastas good as the manual measurements. Moreover,the estimation error (0.03) using the automaticapproach is significantly (p < 0.01) lesser than theinter-expert variability (0.11). These show that oursimple approach gives accurate measurements andcan replace the manual system.

TABLE I: Results from Experiments

No. κ E1 E2 E A d1 d21 0.66 5.10 4.84 4.97 4.97 0.07 0.002 0.62 4.90 4.69 4.80 4.96 0.04 0.033 0.73 4.57 4.13 4.35 4.29 0.19 0.004 0.80 3.41 3.23 3.32 3.53 0.03 0.045 0.82 3.16 3.08 3.12 3.23 0.01 0.016 0.76 5.18 5.05 5.12 5.00 0.02 0.017 0.61 4.98 4.61 4.80 4.67 0.14 0.028 0.82 4.07 3.90 3.99 4.11 0.03 0.029 0.83 3.86 3.72 3.79 3.77 0.02 0.0010 0.76 3.99 3.54 3.77 3.97 0.20 0.0411 0.63 4.63 4.04 4.34 4.23 0.35 0.0112 0.80 4.14 4.31 4.23 4.23 0.03 0.0013 0.84 2.65 2.56 2.61 2.30 0.01 0.0914 0.73 3.92 3.57 3.75 3.48 0.12 0.0715 0.73 4.11 3.50 3.81 3.55 0.37 0.0716 0.49 4.44 4.25 4.35 4.16 0.04 0.0317 0.77 4.07 3.43 3.75 3.55 0.41 0.0418 0.72 2.63 2.38 2.51 2.39 0.06 0.0119 0.85 2.65 2.51 2.58 2.41 0.02 0.0320 0.81 2.50 2.46 2.48 2.43 0.00 0.00Avg 0.74 4.02 3.69 3.82 3.76 0.11 0.03SD 0.09 0.84 0.80 0.81 0.85 0.13 0.03

The Gestational age predicted with our methodallowed the women to be better prepared for theirdelivery days, hence, reducing the number of homedeliveries [11].

IV. DISCUSSIONS

Rather than obtaining a perfect segmentation ofthe femur, the goal is to obtain a bounding boxwith length as close as possible to femur lengthsobtained by sonographers. This approach has beentested using low-cost settings such as low-cost

Fig. 2: From left to right: original image, segmented femur, and predicted gestational age.

portable ultrasound and image acquisition undersub-optimal conditions.With our fully automaticapproach which gives consistent measurements,the issue of operator dependent femur length ac-quisition is addressed.

Future works include the analysis of other bio-metric features linked to the gestational age andthe correlation to the weight to assess the well-being of the fetus [15]. Regarding the gestationalage prediction, some authors argue that there isno evidence that ethnicity significantly influencethe measurements, though no agreement has beenreached [14]. A larger study with bigger populationcan prove the validity of the proposed approach forlow-income countries in West-Africa.

V. CONCLUSIONS

The proposed approach integrating several im-age processing steps obtain the femur length andsubsequently estimate the gestational age, to al-low user-independent fetal feature assessment. Inparticular, we investigated the feasibility of themethod in low-cost settings and on a small West-African population. The method is fully automaticand requires little training to be used by physiciansand/or sonographers.

ACKNOWLEDGMENT

The authors are thankful to Dr. Kojo Pieter-son for the clinical support. This research is partof the cooperation project carried out in ruralcommunities in Ghana called Docmeup by ETH-Global and the African Institute for MathematicalSciences in Ghana [11]. More resources related tothe cooperation project can be found on the websitewww.docmeup.org.

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