Experience of using of machine learning methods to identify the left ventricle region in echocardiographic records

Bobkova, A. O.; Zyuzin, Vasily; Поршнев, С. В.; Bobkov, V V

doi:10.1109/icaict.2016.7991826

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Most of medical research propose median filter to remove speckle noise in image. The other inventions focus on classifying the echocardiogram become some of specific condition [8][9]. Current research, not only 2-dimensional echocardiogram, but also 3-dimensional echocardiographic image.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Motions Classification on Sequential PSAX Echocardiogram

Aziz¹,

Sigit²,

Basuki³

et al. 2018

IJEECS

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<p>Cardiac wall motions classification on 2-dimensional (2D) echocardiographic images is an important issue for quantitative diagnosiing of heart disease. Unfortunately, the bad quality of echocardiogram cause computationally classification on cardiac wall motions is still become a big homework for many researchers to provide the best result. Echocardiogram is produced by soundwaves which absolutely make its images have speckle noise in different intensity. Therefore, this research improves a set of methodology to classify cardiac wall motion semi-automatically. Raw echocardiogram will be enhanced and segmented to take the boundary of endocardium of left ventricular in PSAX cardiac images. New improvement of Semi-automatically methodology is approach on detecting the contour of endocardium and will be inputed as good features in Lucas-Kanade Optical Flow in all sequential echocargraphic images. On classifying cardiac wall motions, this research proposes two important features including length of displacement and flow direction. New proprosed flow determination algorithm and Euclidean distance is used to calculate those features. All the features will be trained by Neural Network (NN) and validated by Leave One Out (LOO) to get accurate result. NN method, which is validated by LOO, has the best result of 81.82% correctness than the other compared methods.</p>

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Section: Introductionmentioning

confidence: 99%