Semi-automatic detection of the left ventricular border

Reis, Maria do Carmo dos; Rocha, Adson Ferreira da; Vasconcelos, Daniel França; Espinoza, Bruno Luiggi M.; Nascimento, Francisco Assis de Oliveira; Carvalho, João L. A.; Salomoni, Sauro Emerick; Camapum, Juliana F.

doi:10.1109/iembs.2008.4649129

Cited by 17 publications

(4 citation statements)

References 8 publications

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“…A similar algorithm for automatic segmentation of dynamic 2-chamber short-axis images is proposed in Refs. [ 9 , 31 ]. A methodology that aids medical professionals in diagnosing cardiac pathologies based on the area and/or volume variation curves may be a natural continuation of this study.…”

Section: Resultsmentioning

confidence: 99%

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Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

et al. 2010

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BackgroundTwo-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.MethodsThis work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.ResultsThe proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated.ConclusionsThe proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

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Section: Resultsmentioning

confidence: 99%

“…Dos Reis et al . [ 9 ] proposed a time-averaging denoising stage based on motion estimation for frame rejection. Amorim et al .…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

et al. 2010

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“…Some studies have attempted to enhance 2-D cardiac ultrasound images by averaging the intensity levels from temporally consecutive frames (Achmad et al 2009;dos Reis et al 2008dos Reis et al , 2009Li et al 1994;Petrovic et al 1986). Because of the constant motion of the heart, the consecutive frames are partially decorrelated, and consequently, spatial compounding can reduce noise and speckle in the processed images.…”

Section: Introductionmentioning

confidence: 99%

Temporal Compounding: A Novel Implementation and Its Impact on Quality and Diagnostic Value in Echocardiography

Perperidis

Cusack

White

et al. 2015

Ultrasound in Medicine & Biology

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Temporal compounding can be used to suppress acoustic noise in transthoracic cardiac ultrasound by spatially averaging partially decorrelated images acquired over consecutive cardiac cycles. However, the reliable spatial and temporal alignment of the corresponding frames in consecutive cardiac cycles is vital for effective implementation of temporal compounding. This study introduces a novel, efficient, accurate and robust technique for the spatiotemporal alignment of consecutive cardiac cycles with variable temporal characteristics. Furthermore, optimal acquisition parameters, such as the number of consecutive cardiac cycles used, are derived. The effect of the proposed implementation of temporal compounding on cardiac ultrasound images is quantitatively assessed (32 clinical data sets providing a representative range of image qualities and diagnostic values) using measures such as tissue signal-to-noise ratio, chamber signal-to-noise ratio, tissue/chamber contrast and detectability index, as well as a range of clinical measurements, such as chamber diameter and wall thickness, performed during routine echocardiographic examinations. Temporal compounding (as implemented) consistently improved the image quality and diagnostic value of the processed images, when compared with the original data by: (i) increasing tissue and cavity signal-to-noise ratios as well as tissue/cavity detectability index, (ii) improving the corresponding clinical measurement repeatability and inter-operator measurement agreement, while (iii) reducing the number of omitted measurements caused by data corruption.

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“…High-boost filtering and Laplacian of the Gausian (LoG) operator is applied to increase image contrast (Maria et al, 2008). In the study of improvement of nuclear track density measurements are evaluated the well known edge detection methods; derivative operators (i.e.…”

Section: Introductionmentioning

confidence: 99%

Automatic Boundary Detection of Wall Motion in Two-dimensional Echocardiography Images

Dawood

Rahmat

Dimon

et al. 2011

Journal of Computer Science

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Problem statement:Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is "important clinical diagnosis parameter for many cardiovascular diseases". A challenge most researchers faced is how to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the pre-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain features of interest (i.e., myocardial tissue). In the second stage, we applied the segmentation process using thresholding technique by considering a mean value of pixels intensity as a threshold value to distinct image features (e.g., Background and object). After thresholding implementation, the two most common mathematical morphology operators 'erosion' and 'dilation' are applied to improve the efficiency of the wall boundary detection process. Finally, Robert's operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of the proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance accuracy percentages are 50% more acceptable and 42% better than QLab system results.

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Semi-automatic detection of the left ventricular border

Cited by 17 publications

References 8 publications

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

Temporal Compounding: A Novel Implementation and Its Impact on Quality and Diagnostic Value in Echocardiography

Automatic Boundary Detection of Wall Motion in Two-dimensional Echocardiography Images

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