Improved echocardiography segmentation using active shape model and optical flow

Sigit, Riyanto; Roji, Calvin Alfa; Harsono, Tri; Kuswadi, Son

doi:10.12928/telkomnika.v17i2.11821

Cited by 17 publications

(15 citation statements)

References 10 publications

(10 reference statements)

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“…Active contour model has been used to segment and delineate boundaries in different types of medical images such as echocardiographs [13], MRI [14], and Computed Tomography (CT) [15]. It is a popular class of image segmentation and boundary delineation method due to its ability to fit a curve to an object boundary by iteratively expanding or contracting its boundary estimate.…”

Section: Active Contour Modelmentioning

confidence: 99%

Contour evolution method for precise boundary delineation of medical images

et al. 2020

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Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.

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Section: Active Contour Modelmentioning

confidence: 99%

Contour evolution method for precise boundary delineation of medical images

et al. 2020

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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%

“…Several previous studies have carried out the application of the tracking process, which aims to visualize the movement of the left ventricle. 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.…”

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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“…The results show that the watershed transform performs better than active contour. Sigit [5] segmented the heart cavity automatically on the short axis view using the active shape model method. 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].…”

Section: Introductionmentioning

confidence: 99%

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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Improved echocardiography segmentation using active shape model and optical flow

Cited by 17 publications

References 10 publications

Contour evolution method for precise boundary delineation of medical images

Contour evolution method for precise boundary delineation of medical images

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

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

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