Automated Fetal Brain Localization, Segmentation, and Abnormalities Detection Through Random Sample Consensus
- Title
- Automated Fetal Brain Localization, Segmentation, and Abnormalities Detection Through Random Sample Consensus
- Creator
- Vijayalakshmi S.; Durgadevi P.; Gayathri S.P.; Shariff A.S.M.
- Description
- The ability to detect and identify prenatal brain abnormalities using magnetic resonance imaging (MRI) is critical, as one in every 1000 women is pregnant with one. The brain is abnormal. Detection of embryonic brain abnormalities at an early stage machine learning techniques can help you increase the quality of your data. Treatment planning and diagnosis according to the literature that the majority of the research done in order to classify brain abnormalities in the term "very early age" refers to preterm newborns and neonates, not fetal development. However, studies of prenatal brain MRI imaging have been published and compared these images to the MRI scans of newborns to identify a non-fetal aberrant behavior in neonates. In this case, a pipeline procedure, on the other hand, is time-consuming. In this research, a machine learning-based pipeline process for fetal brain categorization (FBC) is proposed. The classification of fetal brain anomalies at an early stage, before the baby is delivered, is the paper's key contribution. The proposed approach uses a flexible and simple method with cheap processing cost to detect and categorize a variety of abnormalities from MRI images with a wide range of fetal gestational age (GA). Segmentation, augmentation, feature extraction, and classification and detecting anomalies of the fbrain are different phases of the recent method. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-462, pp. 495-504.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Abnormalities; Automatic; Fetal MRI; Head localization; Machine learning; Random sample consensus (RANSAC); Region of interest (ROI); Segmentation
- Coverage
- Vijayalakshmi S., Christ University, Bangalore, India; Durgadevi P., Galgotias College of Engineering and Technology, Greater Noida, India; Gayathri S.P., Gandhigram Rural Institute (Deemed to be University), Dindigul, India; Shariff A.S.M., Galgotias College of Engineering and Technology, Greater Noida, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981192210-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Vijayalakshmi S.; Durgadevi P.; Gayathri S.P.; Shariff A.S.M., “Automated Fetal Brain Localization, Segmentation, and Abnormalities Detection Through Random Sample Consensus,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20293.