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Statistical Measurement of Ultrasound Placenta Images Complicated by Gestational Diabetes Mellitus Using Segmentation Approach.
G. Malathi,V. Shanthi +1 more
- Vol. 2, pp 332-343
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TLDR
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.Abstract:
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.read more
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References
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Proceedings ArticleDOI
Characterization of ultrasonic images of the placenta based on textural features
TL;DR: The task of classifying ultrasonic images of the placenta according with the gradation proposed by Grannum (1979) is attempted and the ability of a decision tree classifier to discriminate different textures with three sets of textural features was tested.
Real-time video object segmentation algorithm based on change detection and background updating
TL;DR: Wang et al. as discussed by the authors proposed an efficient real-time video object segmentation algo-rithm based on change detection and background updating, which used the change detection technique to analyze temporal information between successive frames for extracting the change region.
Proceedings ArticleDOI
Wavelet Based Features for Ultrasound Placenta Images Classification
G. Malathi,V. Shanthi +1 more
TL;DR: This pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetics by considering the stereo image mapping based on wavelet analysis for 2D reconstruction.
Journal ArticleDOI
Thickness Based Characterization of Ultrasound Placenta Images Using Regression Analysis
G. Malathi,V. Shanthi +1 more
TL;DR: This pilot study involves the feasibility for classifying the ultrasound images ofplacenta with complicating diabetes based on placenta thickness using statistical textural features.
A real-time video object segmentation algorithm based on change detection and background updating
TL;DR: An efficient automatic video object segmentation algorithm by combining change detection with background updating and background subtraction is proposed, which can maintain a high accuracy upper 95 percent with capturing situations of the fixed camera.