.png){kind=icon}
.png){kind=icon}
Return
Echocardiography image segmentation using semi-automatic numerical optimisation method based on wavelet decomposition thresholding
.png){kind=icon}
PRE
AI
DOI：10.1002/ima.22631.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose optimisation based algorithm using wavelet decomposition for segmentation of echocardiography images. The objective of the proposed method is to find an optimal threshold value for segmentation. This threshold value is used for separating the left ventricle by segmenting wavelet decomposed coefficients. The proposed method evaluates the optimal value of threshold using a nonlinear derivative free optimising algorithm. Its objective function is the contrast property of GLCM. The contrast would be highest at the correct position of the boundary between image regions. Accordingly, optimization task is formulated as a maximization problem. The proposed method is an iterative process for finding optimum threshold. Segmentation is carried out using this optimised threshold value. Further, the exact left ventricle contour is extracted using morphological operations and user provided location input. The proposed method is observed to be performing better and found to have 94% accuracy compared to ground truth labels.
Keywords:
echocardiography
image segmentation
optimisation
thresholding
wavelet transform
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
2.1K
Citations:
2.3K
Organization
No organization information available