Automatic left ventricular centre point extraction in echocardiographic images

We present a fully automatic method for obtaining an initial estimate of endocardial border in short axis echo cardiographic videos at two different levels of left ventricle (LV). The geometry of the acoustic window along with circular Hough transform and image statistics is used to robustly identify the region of interest, which encloses left ventricle, irrespective of image quality and level of left ventricle at which image was acquired. Again in the region of interest, left ventricle center point (LVCP) was identified by image statistics. The approximate LV border was detected by radial gradient search with magnitude as well as direction and then smoothing it locally and temporally. The results were validated by comparing computer generated boundaries to those manually outlined by one expert on ten data sets each containing 28 frames over full cardiac cycle. The mean error in the boundaries was ±2mm. Directional radial gradient search along with temporal constraints from adjacent frames gives a better initial estimate of the LV boundary. The contour for each of the frame so obtained may serve a good initialization to optimize it by automated methods based on active contours.

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“…The LVCP was carried out by many researchers [8] and [10]- [11]. The Morphological approach as presented in [8] is better suited Fig.…”

Section: Lvcp Determinationmentioning

confidence: 99%

“…In [10], the LVCP estimate is presented using matched circular arc filters. In [11] authors have presented a novel approach of LVCP detection based on fuzzy sets and template matching. Three common characteristics of the LVCP in short axis echo images under consideration are:…”

Section: Lvcp Determinationmentioning

confidence: 99%

Automatic Detection of Left Ventricle in Echocardiographic Sequences using Radial Search and Temporal Smoothing

Bansod

Desai

Burkule³

2007

2007 15th International Conference on Digital Signal Processing

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“…A complete cardiac cycle is input and each frame is initially analyzed. An automatic fuzzy based method for estimating the location of the Left Ventricular Centre Point (LVCP) in a single frame of 2DE images was previously developed and presented in [12]. The method incorporates knowledge about the left ventricular geometry, position in the standard echo views, and the image intensity information in a fuzzy based framework to define a few number of candidate pixels for LVCP.…”

Section: Automatic Echocardiographical Analyzermentioning

confidence: 99%

“…The method incorporates knowledge about the left ventricular geometry, position in the standard echo views, and the image intensity information in a fuzzy based framework to define a few number of candidate pixels for LVCP. The robustness of the fuzzy reasoning based Center Detection process has been described in [12]. …”

Section: Automatic Echocardiographical Analyzermentioning

confidence: 99%

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Automatic echocardiographical feature extraction for left ventricular wall motion and volume changes visualization

Soraghan

Setarehdan

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-This paper describes a new fully automatic fuzzy multiresolution-based system for cardiac left ventricular (LV) sequence analysis. The edge detection process utilized in the system uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries for all frames in a cardiac cycle. The raw extracted boundaries are processed in the wavelet domain and finally scale-space filtering is used to define the close LV boundaries. The system is used to automatically extract ejection fractions and volume changes for real short axis echocardiographic data. The dynamics of the LV wall are also visualized.

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Advanced Algorithmic Approaches to Medical Image Segmentation

Suri¹,

Setarehdan²,

Singh³

2002

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Automatic left ventricular centre point extraction in echocardiographic images

Cited by 12 publications

References 26 publications

Automatic Detection of Left Ventricle in Echocardiographic Sequences using Radial Search and Temporal Smoothing

Automatic Detection of Left Ventricle in Echocardiographic Sequences using Radial Search and Temporal Smoothing

Automatic echocardiographical feature extraction for left ventricular wall motion and volume changes visualization

Advanced Algorithmic Approaches to Medical Image Segmentation

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