TY - GEN
T1 - A method for effective sequence searching of algorithms processing medical data during segmentation of anatomical structures of the heart
AU - Danilov, V. V.
AU - Skirnevskiy, I. P.
AU - Gerget, O. M.
N1 - Publisher Copyright:
© GraphiCon 2017 - Computer Graphics and Vision.All right reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - The paper considers a method for determining the effective sequence of steps during the processing of medical images, in particular, related to the problem of delineation the anatomical structures of the heart. First, we proposed the metric called the Optimality Index (OI) that is a weighted average of several accuracy coefficients, indices, and mean processing time. The metric allows to estimate how fast and accurate each image processing algorithm is. Moreover, the Optimality Index varies from 0 to 1, which facilitates the comparison of different approaches. The second thing is concerned with comparison of filtering, sharpening, and segmentation technique. All the obtained results are analysed and interpreted by dint of the new metric (OI). During noise reduction step, we compared Median filter, Total Variation filter, Bilateral filter, Curvature Flow filter, Nonlocal Means filter, and Mean Shift filter. To clarify the borders of anatomical structures we used Linear sharpen. Lastly, we applied Watershed segmentation, Clusterization, Region growing segmentation, Morphological segmentation, Image Foresting segmentation, and Isoline delineation as segmentation techniques. All the research studies were performed on a static frame from a series of images obtained as part of an echocardiographic study of the heart.
AB - The paper considers a method for determining the effective sequence of steps during the processing of medical images, in particular, related to the problem of delineation the anatomical structures of the heart. First, we proposed the metric called the Optimality Index (OI) that is a weighted average of several accuracy coefficients, indices, and mean processing time. The metric allows to estimate how fast and accurate each image processing algorithm is. Moreover, the Optimality Index varies from 0 to 1, which facilitates the comparison of different approaches. The second thing is concerned with comparison of filtering, sharpening, and segmentation technique. All the obtained results are analysed and interpreted by dint of the new metric (OI). During noise reduction step, we compared Median filter, Total Variation filter, Bilateral filter, Curvature Flow filter, Nonlocal Means filter, and Mean Shift filter. To clarify the borders of anatomical structures we used Linear sharpen. Lastly, we applied Watershed segmentation, Clusterization, Region growing segmentation, Morphological segmentation, Image Foresting segmentation, and Isoline delineation as segmentation techniques. All the research studies were performed on a static frame from a series of images obtained as part of an echocardiographic study of the heart.
KW - Cardiac ultrasound
KW - Echocardiography
KW - Filtering
KW - Heart
KW - Segmentation
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M3 - Conference contribution
AN - SCOPUS:85078423338
T3 - GraphiCon 2017 - 27th International Conference on Computer Graphics and Vision
SP - 258
EP - 263
BT - GraphiCon 2017 - 27th International Conference on Computer Graphics and Vision
PB - GraphiCon Scientific Society
T2 - 27th International Conference on Computer Graphics and Vision, GraphiCon 2017
Y2 - 24 September 2017 through 28 September 2017
ER -