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

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

doi:10.11591/ijeecs.v18.i2.pp1057-1065

Cited by 6 publications

(13 citation statements)

References 17 publications

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“…As demonstrated in Table 5, the proposed model outperforms compared to the existing work. [5] 2019 NB, RF 86.81% Wan Hajarul [6] 2018 DT and RF 82.99% with RF Amin Ul Haq [8] 2018 SVM, DT, RF, NB, DT 86% with SVM Kathleen H. Miaoa [11] 2018 Deep neural network 83.67% Wiharto Wiharto [12] 2019 Ensemble classifier 88.33% Noor Basha [18] 2019 KNN, NB, SVM, DT 85%, with KNN Edsel Ing [19] 2019 SVM and LR 82.71% with LR Márcio Dias [20] 2020 SVM 87.71% Khaled Mohamad [21] 2020 SVM, NB 84.19% with SVM Pooja Rani [22] 2021 NB, LR, NB, SVM, RF 84.79% with SVM Suja Panicker [23] 2020 SVM 90% G. Magesh [24] 2020 RF 89.30% Ashir Javeed [25] 2020 Deep neural network 91.83% G. Saranya [ A hybrid approach to medical decision-making: diagnosis of heart disease … (Tamilarasi Suresh) 1837 5. CONCLUSION Automated intelligent approaches are crucial for timely prediction of heart disease.…”

Section: Comparative Studymentioning

confidence: 99%

“…In recent years, heart disease have become one of the foremost reason of chronic disease related deaths thought the world population [1]- [5]. Moreover, heart disease is among the most frequently occurring diseases in the world affecting 26 million of the world population [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have developed intelligent model with focus on improving the performance of heart disease prediction model and there exist model for heart disease prediction in literature. However, there is still larger scope for improving the performance of the existing model for heart disease prediction [5]. Thus, in this study an effort has been made in designing and implementing an effective and more accurate model that classifies a given sample in  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

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Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

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Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

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“…In this study, good features on the left ventricular wall were used for the tracking process with the optical flow using the Lucas-Kanade method. Reference [16], using good features obtained from crossing lines on the contours is used to track left ventricles. The proposed method shows the results of calculations with a sensitivity of 90% and an accuracy of 87.451%.…”

Section: Fig 2 Triangle Equationmentioning

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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“…It uses an initialization point located in the left ventricular wall to visualize tracking in a four-chamber viewpoint. Anwar et al [7] propose a method for developing automatic heart wall cavity tracking using Lucas-Kanade optical flow. The tracking method uses a good-feature point distribution defined in the first frame using an image processing approach with a sensitivity of 90 % and an accuracy of 87.451 %.…”

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

Cited by 6 publications

References 17 publications

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

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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