Improved ejection fraction measurement on cardiac image using optical flow

Pratiwi, Arvina Aulia; Sigit, Riyanto; Basuki, Dwi Kurnia; Oktaviono, Yudi Her

doi:10.1109/kcic.2017.8228602

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…Optical flow works on several assumptions, the pixel intensities of an object do not change between consecutive frames and neighboring pixels have similar motion. Where, consider a pixel ( , , ) I x y t in first frame and it moves by distance ( , ) xy  in next frame taken after t  , thus since those pixels are the same and intensity does not change, can be expressed by (12).…”

Section: Optical Flowmentioning

confidence: 99%

“…Sigit [10,11] semi-automatic segmentation using initialization point as a good feature for tracking the movement of the heart using optical flow. This method is also used in the apical four-chamber view heart movement tracking system which can reconstruct the heart cavity wall lines with a semi-automatic system [12]. Aziz [13] proposed the development on echocardiography images to segmentation take the boundary of endocardium of left ventricular in short axis cardiac, however still semi-automatic for approach on detecting the contour of endocardium as good features in Lucas-Kanade 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: Optical Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Anwar

Sigit

Basuki

et al. 2020

IJEECS

Self Cite

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“…C. Feature Extraction Based on previous studies, the use of good features to track the left ventricular obtained good results [9], [10]. This study uses a good feature to track the movement of diastole to systole in the left ventricle.…”

Section: Fig 2 Triangle Equationmentioning

confidence: 99%

“…Reference [9] performed the process of segmenting the left ventricle on the short axis automatically using the active shape model method and described the visualization of tracking using semi-automatic optical flow with the initialization of points on the cavity. In reference [10], a semi-automatic left ventricular contour formation process was conducted; the process proceeded to the tracking process using optical flow to calculate the volume of left ventricular function. The semi-automatic approach in tracing the process for the classification of heart movements in the short axis viewpoint is seen in [11].…”

Section: Introductionmentioning

confidence: 99%

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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“…Sigit et al [5] visualize the tracking of short-axis parts using a semiautomatic approach; the tracking uses contour assistance obtained from segmentation results with the active shape model method. A semi-automatic approach has also been carried out in [6]. It uses an initialization point located in the left ventricular wall to visualize tracking in a four-chamber viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Left Ventricular Movement Feature Extraction: A New Method for Classifying Heart Condition in Four-Chamber and Two-Chamber Views

2022

IJIES

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The American heart association recommends examining four heart angles, i.e., the long axis, short axis, two-chamber, and a four-chamber section of the left ventricle, during the inspection of hearts' condition. Currently, left ventricle observation to assess the heart's condition is commonly done manually. No studies have performed an extraction of features from the movement of the left ventricular wall in the four-chamber and two-chamber views. The presence of an automatic method in observing and evaluating the left ventricle condition will help experts diagnose heart conditions. Methods: This study proposes an automatic tracking method for the left ventricular heart cavity, thereby obtaining the value of heart movement to build a classification system. It involves several stages: image preprocessing, image segmentation, tracking, feature extraction, classification, and validation. Results: The initial preprocessing stage produces images that have clear differences between the heart wall and the left ventricle. This clear distinction becomes the characteristic in separating the left ventricle from the heart wall, thus obtaining a contour using the segmentation method. The obtained contour line is then used as a good-feature using the intersection line. This research uses 24 good-features points which are only defined in the first frame. Furthermore, all frames will be processed using the optical flow Lucas-Kanade method to track the movement of the heart wall. Conclusion: Each good-feature produces four features values: direction (positive and negative) and distance (positive and negative); thus, a total of 96 features are obtained for the entire process. In its implementation, the support vector machine method obtains the highest accuracy value of 94,762 % and 94,977 % with validation techniques, k-folds, and leave-one-out. Significance: This study proposes a method for extracting features from left ventricular movement.

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Improved ejection fraction measurement on cardiac image using optical flow

Cited by 7 publications

References 7 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

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

Left Ventricular Movement Feature Extraction: A New Method for Classifying Heart Condition in Four-Chamber and Two-Chamber Views

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