scholarly journals FETAL CARDIAC STRUCTURE DETECTION FROM ULTRASOUND SEQUENCES

2012 ◽  
pp. 53-57
Author(s):  
RESHMI MARIAM REJI JACOB ◽  
S. PRABAKAR ◽  
DR.K. PORKUMARAN
Keyword(s):  
Clustering Algorithm ◽  
Fetal Heart ◽  
Heart Defects ◽  
Region Of Interest ◽  
Appearance Model ◽  
Cardiac Structure ◽  
Structure Detection ◽  
Heart Abnormalities ◽  
Novel Method ◽  
Effective Diagnosis

Fetal heart abnormalities are the most common congenital anomalies and are also the leading cause of infant mortality related to birth defects. More than one-third of all malformations found after delivery are congenital heart defects. The prenatal detection of fetal cardiac structure is difficult because of its small size and rapid movements but is important for the early and effective diagnosis of congenital cardiac defects. A novel method is proposed for the detection of fetal cardiac structure from ultrasound sequences. An initial pre-processing is done to remove noise and enhance the images. An effective K means clustering algorithm is applied to the images to segment the region of interest. Finally an active appearance model is proposed to detect the structure of fetal heart.

Computer-Aided Fetal Cardiac Scanning using 2D Ultrasound

2012 ◽  
Vol 1 (1) ◽  
pp. 1-13 ◽  
Author(s):  
N. Sriraam ◽  
S. Vijayalakshmi ◽  
S. Suresh
Keyword(s):  
Fetal Heart ◽  
Region Of Interest ◽  
Well Being ◽  
Experimental Simulation ◽  
Morphological Filters ◽  
Fetal Biometry ◽  
Heart Chamber ◽  
Computer Aided ◽  
2D Ultrasound ◽  
Heart Abnormalities

Fetal heart biometry is an indicator for providing information about the presence of heart chambers, their growth, and well being. As a clinical routine, ultrasonic scanning based fetal biometry is performed during the second trimester by skilled specialists. Such procedures are often considered tedious and time consuming. Detection of congenital heart abnormalities, such as septal defects, affects the proper functioning of the heart during the growth of the fetus, and such defects can be identified if the fetal heart structure and its features like size, shape, and symmetry are monitored. Recently, attempts have been made to provide computer-aided automated procedure where the performance depends on the efficacy of the developed algorithms. This work focuses on computer aided automated fetal cardiac scanning using 2-D ultrasonic imaging from fetal heart biometry. The process involves extracting frames from the cine-loop sequences followed by removal of noise using morphological filters. The chamber region is recognized by introducing automated region of interest (ROI). Experimental simulation study demonstrates the efficiency of algorithm in detecting the shape of each chamber. The identified chamber shape will further facilitate in automated measurement of fetal heart chamber and thus reduces the qualitative visualization errors.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

Collaborative Study on 4-Dimensional Echocardiography for the Diagnosis of Fetal Heart Defects

2010 ◽  
Vol 29 (11) ◽  
pp. 1573-1580 ◽  
Author(s):  
Jimmy Espinoza ◽  
Wesley Lee ◽  
Christine Comstock ◽  
Roberto Romero ◽  
Lami Yeo ◽  
...  
Keyword(s):  
Fetal Heart ◽  
Heart Defects ◽  
Collaborative Study ◽  
Dimensional Echocardiography

scholarly journals Cardiovascular Diseases Prediction Using Machine Learning Algorithms

2021 ◽  
Vol 12 (6) ◽  
pp. 1083-1088
Author(s):  
Rumana M Shaikh
Keyword(s):  
Heart Disease ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Heart Defects ◽  
Heart Rhythm ◽  
Machine Learning Algorithms ◽  
Restricted Boltzmann Machines ◽  
Heart Attacks ◽  
Broad Variety ◽  
Heart Abnormalities

A broad variety of health conditions are involved in heart disease. Several illnesses and disorders come under the heart disease umbrella. Heart disease forms include: In arrhythmia, abnormality of the heart rhythm. Arteriosclerosis, Hardening of the arteries is atherosclerosis. Via cardiomyopathy, this disorder causes muscles in the heart to harden or grow weak. Defects of the congenital heart, heart abnormalities that are present at birth are congenital heart defects. Disease of the coronary arteries (CAD), the accumulation of plaque in the heart's arteries triggers CAD. It's called ischemic heart disease occasionally. Infections of the heart, bacteria, viruses, or parasites may trigger heart infections. Heart diseases namely arrhythmias, coronary heart disease, heart attacks, cardiomyopathy will be detect using the proposed algorithm in this paper. Here I compared three algorithms namely Restricted Boltzmann Machines, Deep Belief Networks and Convolutional Neural Networks for electrocardiogram (ECG) classification for heart disease.

Ultrasound examination of pregnant women in diagnosing fetal cardiac pathology

2020 ◽  
Vol 69 (2) ◽  
pp. 43-50
Author(s):  
Viktoria A. Lim
Keyword(s):  
Pregnant Women ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Heart Defects ◽  
Care Service ◽  
Ultrasound Screening ◽  
Atrioventricular Canal ◽  
Cardiac Pathology ◽  
Organ Systems ◽  
Atrioventricular Canal Defect

Hypothesis/aims of study. Fetal heart defects are the most common malformations causing infant mortality. The task of the obstetric care service is to make a timely diagnosis, which includes high-quality ultrasound screening and, if necessary, fetal echocardiography. This study aimed to compare fetal echocardiography with postpartum echocardiography. Study design, materials and methods. 101 pregnant women with both isolated fetal heart defects and combined pathology were examined for the period 20172019. Results. The greatest number of heart defects was detected at 2331 weeks of gestation. The structure of the malformations is diverse, the most common one being a complete form of the atrioventricular canal defect. In multiple pregnancies, complex heart defects were often combined with abnormalities in other organ systems. Conclusion. It is recommended to describe the heart structure in detail from 2122 weeks of pregnancy. If cardiac pathology is detected in utero, it is mandatory to conduct an examination of other fetal organs.

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