Automatic Segmentation of Heart Cavity in Echocardiography Images: Two &amp; Four-Chamber View Using Iterative Process Method

Anwar, Anwar; Sigit, Riyanto; Basuki, Achmad; Gunawan, I Putu Adi Surya

doi:10.1109/elecsym.2019.8901613

Cited by 5 publications

(16 citation statements)

References 12 publications

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“…The use of a median filter deemed suitable for this case. The working principle of the median filter is spatial processing to determine the pixels in the image that are affected by speckle noise [8]. After the speckle noise was reduced, the clarity of the image still needs to improve, to clarify the part between the wall and the heart cavity.…”

Section: Image Enhancementmentioning

confidence: 99%

“…Furthermore, the whole frame will be processed by optical flow method. Image segmentation stage uses thresholding, canny, region area and collinear methods to get the contour of the heart cavity [8]. Global thresholding method was used to perform segmentation of the heart cavity.…”

Section: Image Segmentationmentioning

confidence: 99%

“…The Canny filter is an algorithm that used to detect edges on shape objects contained in an image. Canny filters can be expressed by (7)(8)(9)(10)…”

Section: Image Segmentationmentioning

confidence: 99%

“…A study of conducted segmentation automatically using the triangle method, this method segmentation obtained the smallest value of the error evaluation of 8.18%, the snake method of 19.94% and the watershed of 15.97% [6,7]. Anwar [8] proposed the development of a high-boost filter method to improve the results of heart segmentation in two and four chambers, and obtain an accuracy of 89.409%.…”

Section: Introductionmentioning

confidence: 99%

“…Based on previous research that discusses the tracking system in the part of the heart cavity semiautomatically, most of those research have not yet been processed parts of the left ventricle automatically at the view of two-chamber and four-chambers. This research uses an image processing approach [8,13,14] to find good features in echocardiographic videos automatically. Good feature definition consists of image enhancement, segmentation, intersection and tracking processes using the optical flow.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of optical flow: good feature definition for tracking of heart cavity

Anwar

Sigit

Basuki

et al. 2020

IJEECS

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<span>Echocardiography is a method of examination using high-frequency sound waves to capture images of the heart organ structure. Echocardiography video is used by a doctor to analyze heart wall cavity movements and identify heart disease. Several points of view including the long axis, the two and four cavities in the left ventricle can be used in the examination of heart function. Cardiac assessment is still performed conventionally, which requires a level of thoroughness. This research proposes a method for tracking the movement of the heart wall. In this method, the good feature was defined only in the first frame. Furthermore, the whole frame will be processed by the optical flow method. Good feature definition consists of image enhancement, segmentation and tracking processes using the optical flow. Furthermore, the calculation of contour similarity from the proposed method by forming contour manually using 24 point initialization to draw the heart cavity. The proposed method shows the calculation results with a sensitivity of 90% and an accuracy of 87.451%.</span>

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Section: Image Enhancementmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

“…The Canny filter is an algorithm that used to detect edges on shape objects contained in an image. Canny filters can be expressed by (7)(8)(9)(10)…”

Section: Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of optical flow: good feature definition for tracking of heart cavity

Anwar

Sigit

Basuki

et al. 2020

IJEECS

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Comparison of Optical Flow Methods: Study About Left Ventricular Tracking in Multi View Echocardiographic Images

Asyhari

Sigit

Dewantara

et al. 2021

2021 International Electronics Symposium (IES)

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A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity

Sigit¹,

Basuki²,

Anwar³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Echocardiography is a method of examination with high-frequency sound waves to obtain images of heart organs. Examination of heart health conditions with echocardiography as an imaging method, serves to detect the potential for heart disease, thus that the right treatment from the evaluation results can be decided. Examination of the source of heart disease with echocardiography was performed using several views, namely the long axis, short axis, two-chamber, and four-chamber. However, the assessment of cardiac function is still carried out conventionally. Thus it is necessary to build a system that can assess cardiac function. This study proposes a feature extraction method for the classification of heart disease based on the left ventricular motion on the short-axis. In this method, feature extraction uses 24 good features for the process of tracking the movement of the left ventricle with optical flow. Each good feature produces four features, namely direction (negative direction and positive direction) and distance (negative distance and positive distance) from the results of left ventricular tracking and produces 96 attributes for the whole process. The features that have been obtained are then processed using several classification algorithms with validation techniques that are, k-folds, and leave one out. The result is a classification algorithm with a gradient boosting classifier method that has the best accuracy. Gradient boosting classifier produces accuracy values with validation techniques for k-folds 90.98%, and leave one out 93.23%. This shows that the gradient boosting classifier can be relied upon for the classification of heart disease using the proposed feature extraction method. In this study, we developed a new feature extraction method from the results of tracking the heart wall using optical flow. This algorithm can produce feature values from the tracking results that can be used to build a knowledge system for the classification of heart health conditions.

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Automatic Segmentation of Heart Cavity in Echocardiography Images: Two & Four-Chamber View Using Iterative Process Method

Cited by 5 publications

References 12 publications

Implementation of optical flow: good feature definition for tracking of heart cavity

Implementation of optical flow: good feature definition for tracking of heart cavity

Comparison of Optical Flow Methods: Study About Left Ventricular Tracking in Multi View Echocardiographic Images

A New Feature Extraction Method for Classifying Heart Wall from Left Ventricle Cavity

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