Wavelet Based Features for Ultrasound Placenta Images Classification

Malathi, G.; Shanthi, V.

doi:10.1109/icetet.2009.231

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Fetal Cardiac Heart Defects such as heart rate detection, valve defects etc., detected using ECG, Doppler Cardiogram Signals, and Doppler Ultrasound Signals are conveyed in [3, 4, 5, and 7]. These works relay on the approaches like Image Classification [8], recurrence qualification analysis [5], feature extraction etc. The authors in the above works have concentrated on only specific fetal anomalies like heart defects.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Anomalies in Fetus using Convolution Neural Network

H.M¹,

H.T²,

S.P³

2018

IJITCS

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Parental diagnosis is required during midpregnancy period from 18-22 weeks in order to know the well-being of the fetus. This diagnosis is usually done through ultrasound scanning. Ultrasound scanning which is also called as sonogram, is an ultrasound based medical imagining technique used to envision the fetus and its development during the gestation period. If there is an abnormality in the diagnosed fetus then the parents and the doctors can do emergency parental care. Anomalies in Fetus occur before birth. Detecting fetal anomalies is a difficult task since it needs expertise and also requires a considerable amount of time, which will not be convenient at an emergency situation. In order to improve the diagnosis accuracy and to reduce the diagnosis time, it has become a demanding issue to develop an efficient and reliable medical decision support system. In this paper we present machine learning approach, such as convolution neural network which is most commonly applied to examine visual pretense. The main motive behind using CNN is due to their accuracy, fewer memory requirements and better training of images. This approach have shown great potential to be applied in the development of medical decision support system for Fetal anomalies which need immediate care.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Anomalies in Fetus using Convolution Neural Network

H.M¹,

H.T²,

S.P³

2018

IJITCS

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“…However, obtaining standardized head, abdomen, and femoral image planes is difficult due to variable image quality (Ahmed & Noble, 2016). For this reason, many ultrasound image classification studies have been carried out to assist experts in their evaluation (Carneiro et al, 2008;Ishikawa et al, 2019;Kumar et al, 2016;Malathi & Shanthi, 2009;Sinclair et al, 2018;Sobhaninia et al, 2019;Yaqub et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Fetal Movement Detection and Anatomical Plane Recognition using YOLOv5 Network in Ultrasound Scans

Dandıl¹,

Turkan²,

Urfalı³

et al. 2021

European Journal of Science and Technology

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Analyzing medical images and videos with computer-aided algorithms provides important benefits in the diagnosis and treatment of diseases. Especially in recent years, the increasing developments in deep learning algorithms have provided continuous improvement in subjects such as speed, performance and hardware need in the processing of medical data. Examination of medical data, which may require advanced expertise, using deep learning algorithms has begun to be widely used as a secondary tool in the decision-making process of physicians. Tracking the movements of the fetus and recognizing its planes in ultrasound (US) videos is an important parameter in evaluating the health of the baby. In this study, a YOLOv5 deep learning network based method is proposed to identify fetal anatomical planes from fetal ultrasound and to detect their movements. First of all, a dataset of videos containing 16-20 weeks of fetal movements is created in the study. In the next step, the fetal head, arm, heart and body are identified and tracking using the deep-SORT algorithm on the labeled data. In the experimental studies conducted on ultrasound videos within the scope of the study, using the YOLOv5 algorithm, head, body, heart and arm are recognized with 95.04%, 94.42%, 88.31% and 83.23% F1-score, respectively. In addition, ultrasonic video movements of the head, heart and body of the fetus are followed and the trajectories and patterns of the movements are extracted. Thus, the detection of fetal movements from the movement patterns transformed into a two-dimensional plane is achieved.

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“…In a previous work [7], a pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetes by considering the stereo image mapping based on wavelet analysis for 2D reconstruction. The research used wavelet-based methods to extract features from the ultrasonic images of placenta.…”

Section: Introductionmentioning

confidence: 99%

Statistical measurement of ultrasound placenta images using segmentation approach

Malathi

Shanthi

2010

2010 International Conference on Signal and Image Processing

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Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.

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Wavelet Based Features for Ultrasound Placenta Images Classification

Cited by 7 publications

References 14 publications

Detection of Anomalies in Fetus using Convolution Neural Network

Detection of Anomalies in Fetus using Convolution Neural Network

Fetal Movement Detection and Anatomical Plane Recognition using YOLOv5 Network in Ultrasound Scans

Statistical measurement of ultrasound placenta images using segmentation approach

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