LEFT VENTRICULAR SEGMENTATION IN ECHOCARDIOGRAPHY IMAGES BY MANIFOLD LEARNING AND DYNAMIC DIRECTED VECTOR FIELD CONVOLUTION

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MASHHADI N.; BEHNAM H.; SHALBAF A.; ALIZADEH SANI Z.

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Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2014 | Volume:12 | Issue:1 Page(s): 23-32

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Title

LEFT VENTRICULAR SEGMENTATION IN ECHOCARDIOGRAPHY IMAGES BY MANIFOLD LEARNING AND DYNAMIC DIRECTED VECTOR FIELD CONVOLUTION

Author(s)

MASHHADI N. | BEHNAM H. | SHALBAF A. | ALIZADEH SANI Z. | Issue Writer Certificate

Keywords

ACTIVE CONTOURQ2

LEFT VENTRICULARQ2

MANIFOLD LEARNINGQ2

LOCALLY LINEAR EMBEDDINGS ALGORITHMQ2

ECHOCARDIOGRAPHYQ2

Abstract

Cardiac diseases are the major causes of death throughout the world. The study of LEFT VENTRICULAR (LV) function is very important in the diagnosis of heart diseases. Automatic tracking of the boundaries of the LV wall during a cardiac cycle is used for quantification of LV myocardial function in order to diagnose various heart diseases including ischemic disease. In this paper, a new automatic method for segmentation of the LV in ECHOCARDIOGRAPHY images of one cardiac cycle by combination of MANIFOLD LEARNING and ACTIVE CONTOUR based dynamic directed vector field convolution (DDVFC) is proposed. In this method, first ECHOCARDIOGRAPHY images of one cardiac cycle have been embedded in a two dimensional (2-D) space using one of the most popular MANIFOLD LEARNING algorithms named Locally Linear Embeddings. In this new space, relationship between these images is well represented. Then, segmentation of the LV wall during a cardiac cycle is done using ACTIVE CONTOUR based DDVFC. In this method, final contour of each segmented frame is used as the initial contour of the next frame. In addition, in order to increase the accuracy of the LV segmentation and also prevent the boundary distortion, maximum range of the ACTIVE CONTOUR motion is limited by Euclidean distances between consequent frames in resultant 2-D manifold. To quantitatively evaluate the proposed method, echoacardiography images of 5 healthy volunteers and 4 patients are used. The results obtained by our method are quantitatively compared to those obtained manually by the highly experienced echocardiographer (gold standard) which depicts the high accuracy of the presented method.

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APA: Copy

MASHHADI, N., BEHNAM, H., SHALBAF, A., & ALIZADEH SANI, Z.. (2014). LEFT VENTRICULAR SEGMENTATION IN ECHOCARDIOGRAPHY IMAGES BY MANIFOLD LEARNING AND DYNAMIC DIRECTED VECTOR FIELD CONVOLUTION. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 12(1), 23-32. SID. https://sid.ir/paper/228406/en

Vancouver: Copy

MASHHADI N., BEHNAM H., SHALBAF A., ALIZADEH SANI Z.. LEFT VENTRICULAR SEGMENTATION IN ECHOCARDIOGRAPHY IMAGES BY MANIFOLD LEARNING AND DYNAMIC DIRECTED VECTOR FIELD CONVOLUTION. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2014;12(1):23-32. Available from: https://sid.ir/paper/228406/en

IEEE: Copy

N. MASHHADI, H. BEHNAM, A. SHALBAF, and Z. ALIZADEH SANI, “LEFT VENTRICULAR SEGMENTATION IN ECHOCARDIOGRAPHY IMAGES BY MANIFOLD LEARNING AND DYNAMIC DIRECTED VECTOR FIELD CONVOLUTION,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 12, no. 1, pp. 23–32, 2014, [Online]. Available: https://sid.ir/paper/228406/en

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