Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging

Satwika, I Putu; Habibie, Ikhsanul; Ma’sum, M. Anwar; Febrian, Andreas; Budianto, Enrico

doi:10.1109/icacsis.2014.7065898

Cited by 13 publications

(7 citation statements)

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“…Considering the relevance of HC measurement in the clinical practice, several automatic and semi-automatic algorithms have been proposed in the years. First approaches were mainly model-based and relied on randomized Hough transform ( [9], [10]), circular shortest path [11], active contours ( [12], [13]) and Chan-Vese level set [14].…”

Section: Related Workmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intraand inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 (± 1.76) mm and a Dice similarity coefficient of 97.75 (± 1.32) % were achieved, overcoming approaches in the literature. Conclusions The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.

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Section: Related Workmentioning

confidence: 99%

A regression framework to head-circumference delineation from US fetal images

Fiorentino

Moccia

Capparuccini

et al. 2021

Computer Methods and Programs in Biomedicine

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“…This paper is an extension of previous work that combined object detection and shape approximation for fetal organs segmentation [15]. Another approach is using super pixel based classification [16]. In previous study we have also proposed an optimization of ellipse curve approximation using Particle Swarm Optimization (PSO) [17].…”

Section: Figure 1 Intelligent Ultrasound Systemmentioning

confidence: 99%

Automatic Fetal Organs Detection and Approximation in Ultrasound Image

Ma’sum

Jatmiko

Wiweko

et al. 2015

International Journal on Smart Sensing and Intelligent Systems

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“…On the telehealth system development side, the authors believe that this proposed system can be effectively implemented in specific areas where modest internet connection is available but severely lacking the service of gynecologist or ultrasound system availability. This teleultrasound system will be integrated to an integrated telehealth system with tele-cardiology conducted in previous researches [12] [13]. The existence of telehealth system can also provide a low cost health service for the patients by saving the expense of traveling.…”

Section: Introductionmentioning

confidence: 99%

“…This teleultrasound system will be integrated to an integrated telehealth system with tele-cardiology conducted in previous researches [12] [13]. The existence of telehealth system can also provide a low cost health service for the patients by saving the expense of traveling.…”

mentioning

confidence: 99%

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

Jatmiko

Habibie

Ma’sum

et al. 2015

International Journal on Smart Sensing and Intelligent Systems

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Abstract-One of the most profound use of ultrasound imaging is fetal growth monitoring.Conventionally, physicians will perform manual measurements of several parameters of the ultrasound images to draw some conclusion of the fetal condition by manually annotating the fetal images on the ultrasound device interface. However, performing manual annotation of fetal images will require significant amount of time considering the number of patients an obstetrician can have. In this paper, an integrated automatic system for fetal growth monitoring and detection is proposed. This system will be able to automatically measuring the parameters of fetal head, abdomen, femur, and humerus. In addition to automated image detection, we also propose an integrated telehealth monitoring system to provide better access of ultrasound monitoring for patients that lives in rural areas. A new approach of fetal image detection is also proposed by using AdaBoost.MH boosting algorithm that is combined with an improved efficient Hough Transform for detecting ellipse-like organs such as head and abdomen.

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Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging

Cited by 13 publications

References 13 publications

A regression framework to head-circumference delineation from US fetal images

A regression framework to head-circumference delineation from US fetal images

Automatic Fetal Organs Detection and Approximation in Ultrasound Image

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

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