Veins projection performance based on ultrasonic distance sensor in various surface objects

Gunawan, I Putu Adi Surya; Sigit, Riyanto; Gunawan, Agus

doi:10.11591/ijeecs.v17.i3.pp1362-1370

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…However, edge detection using canny-filters usually cannot obtain the desired contour, where there is a small noise contour in some parts of the left ventricle. Reference [7,15] uses the region filter method to remove several contour lines considered noise by calculating each contour's area in echocardiographic images.…”

Section: 𝐼 ∘ 𝐸 = (𝐼 ⊕ (−𝐸)) ⊕ 𝐸mentioning

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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Section: 𝐼 ∘ 𝐸 = (𝐼 ⊕ (−𝐸)) ⊕ 𝐸mentioning

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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“…Detection of edge lines using canny still needs to be improved to eliminate small noise around the left ventricle. The filter region method was able used to eliminate small contours by counting the number of points each contour and providing thresholds with values [14]. However, the results of the region filter have not been able to remove the large contours on the left ventricular side.…”

Section: B Segmentationmentioning

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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Veins projection performance based on ultrasonic distance sensor in various surface objects

Cited by 2 publications

References 22 publications

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

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

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

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