scholarly journals Automatic segmentation of right ventricle on ultrasound images using sparse matrix transform and level set

2013 ◽  
Author(s):  
Xulei Qin ◽  
Zhibin Cong ◽  
Luma V. Halig ◽  
Baowei Fei
Keyword(s):  
Right Ventricle ◽  
Level Set ◽  
Sparse Matrix ◽  
Automatic Segmentation ◽  
Ultrasound Images ◽  
Sparse Matrix Transform

A versatile algorithm for the automatic segmentation of hippocampus based on level set

2011 ◽  
Vol 7 (3) ◽  
pp. 213 ◽  
Author(s):  
J. Rajeesh ◽  
R.S. Moni ◽  
S. Palanikumar ◽  
T. Gopalakrishnan
Keyword(s):  
Level Set ◽  
Automatic Segmentation

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

2021 ◽  
pp. 016173462110425
Author(s):  
Jianing Xi ◽  
Jiangang Chen ◽  
Zhao Wang ◽  
Dean Ta ◽  
Bing Lu ◽  
...  
Keyword(s):  
Congenital Anomaly ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Scale Model ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images

2018 ◽  
Vol 13 (11) ◽  
pp. 1707-1716 ◽  
Author(s):  
M. Villa ◽  
G. Dardenne ◽  
M. Nasan ◽  
H. Letissier ◽  
C. Hamitouche ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images

Validation of an automatic segmentation method to detect vertebral interfaces in ultrasound images

2016 ◽  
Vol 10 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Matteo Aventaggiato ◽  
Maurizio Muratore ◽  
Paola Pisani ◽  
Aimè Lay-Ekuakille ◽  
Francesco Conversano ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images ◽  
Segmentation Method

scholarly journals Biometric Foetal Contour Extraction using Hybrid Level Set

2020 ◽  
Author(s):  
Rachana Jaiswal ◽  
Srikant Satarkar
Keyword(s):  
Feature Extraction ◽  
Level Set ◽  
Medical Images ◽  
Traditional Approach ◽  
Ultrasound Images ◽  
Contour Extraction ◽  
Processing Technologies ◽  
Biometric Measurements ◽  
Ultrasonic Images ◽  
Automatic Technique

In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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