Automated Telehealth System for Fetal Growth Detection and Approximation of Ultrasound Images

Jatmiko, Wisnu; Habibie, Ikhsanul; Ma’sum, M. Anwar; Rahmatullah, Robeth; Satwika, I Putu

doi:10.21307/ijssis-2017-779

Cited by 21 publications

(9 citation statements)

References 27 publications

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“…Systems for automatic HC measurement have been presented using randomized Hough transform [ 4 , 5 ], Haar-Like features [ 6 – 9 ], multilevel thresholding [ 10 ], circular shortest paths [ 11 ], boundary fragment models [ 12 ], semi-supervised patch based graphs [ 13 ], active contouring [ 14 , 15 ], intensity based features [ 16 ] and texton based features [ 17 ]. Although these methods show promising results, they were evaluated on a relatively small amount of data (10 to 175 test images).…”

Section: Introductionmentioning

confidence: 99%

Automated measurement of fetal head circumference using 2D ultrasound images

et al. 2018

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In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, −0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.

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Section: Introductionmentioning

confidence: 99%

Automated measurement of fetal head circumference using 2D ultrasound images

et al. 2018

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“…Past studies have used various methods for HC measurement such as randomized Hough transform [4], semisupervised patch based graphs [5], multilevel thresholding circular shortest paths [6], boundary fragment models [7], Haar-Like features [8], active contouring [9], or compound methods such as [10] which apply Haar-like features to train a random forest classifier in order to locate the fetal skull. Then, HC was extracted by using Hough transform, dynamic programming and ellipse fitting.…”

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confidence: 99%

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

Sobhaninia

Rafiei

Emami

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art. I. INTRODUCTION Ultrasound (US) imaging is a safe non-invasive procedure for diagnosing internal body organs. Ultrasound imaging as compared to other imaging tools, such as computed tomography (CT) and magnetic resonance imaging (MRI), is cheaper, portable and more prevalent [1]. It helps to diagnose the causes of pain, swelling, and infection in internal organs, for evaluation and treatment of medical conditions [2].Ultrasound imaging has turned into a general checkup method for prenatal diagnosis. It is used to investigate and measure fetal biometric parameters, such as the baby's abdominal circumference, head circumference, biparietal diameter, femur and humerus length, and crown-rump length. Furthermore, the fetal head circumference (HC) is measured for estimating the gestational age, size and weight, growth monitoring and detecting fetus abnormalities [3].Despite all the benefits and typical applications of US imaging, this imaging modality suffers from various artifacts such as motion blurring, missing boundaries, acoustic shadows, speckle noise, and low signal-to-noise ratio. This makes the US images very challenging to interpret, which requires expert operators. As shown in US image samples of

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“…For the identification of face, a cascade classifier is composed of several phases. It helps to advance the probability of a face being identified [12]. Then Dlib library [13] is used to extract facial landmarks after a face is detected.…”

Section: Figure 1 Proposed System Framementioning

confidence: 99%

Down syndrome identification and classification using facial features with neural network

VK¹,

Rajesh²

2022

Global J. Eng. Technol. Adv.

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The medical image tool named Ultrasound imaging is used for the analysis of fetus behaviour. Due to the presence of noise the quality of these images is low. Proper filtering is required to suppress this noise. Before the fatal development analysis, it is required image processing method. The processed images are then used for the analysis and it helps to take care of the health. Down syndrome is one of the crucial chromosomal disorders. It is due to the presence of a further copy of chromosome 21. The ultrasound imaging helps to point out DS in the earlier stage of pregnancy by handling the image in an efficient manner. In this work, nasal bone identification and texturing methods are used to detect the disease. Here we designed a nasal bone identification module for the image classification as normal or abnormal. Down syndrome detection utilizes a collection of facial expression images. A compact geometric descriptor is employed for extracting the facial features from the image set. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for Down syndrome prevention.

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Automated Telehealth System for Fetal Growth Detection and Approximation of Ultrasound Images

Cited by 21 publications

References 27 publications

Automated measurement of fetal head circumference using 2D ultrasound images

Automated measurement of fetal head circumference using 2D ultrasound images

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

Down syndrome identification and classification using facial features with neural network

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