Segmentation of the Left Ventricle Using Active Contour Method with Gradient Vector Flow Forces in Short-Axis MRI

Pieciak, Tomasz

doi:10.1007/978-3-642-31196-3_3

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…However, the original GVF failed to separate the big box from the small box for the image at time t, as shown in the middle image of second row, where there are no boundaries between two boxes. In contrast, by incorporating the temporal information between t + 1 and t − 1 using equation (13), the proposed GVF-T can successfully delineate the big box from the small box even when they are merged together at time t, as shown in the middle image of the last row. In this example, we perform RV endocardial segmentations using GVF and GVF-T on cardiac MR images, which are obtained from the right ventricle segmentation challenge website (http://www.litislab.eu/rvsc).…”

Section: Resultsmentioning

confidence: 81%

“…To include (13) into the diffusion of the gradient vector flow, we formulate the energy function of GVF-T by…”

Section: B Gvfmentioning

confidence: 99%

“…Up to now, the GVF has been proven over many external force field methods due to its greater active range, especially the ability to approach the boundary of concave regions. In the past decade, the GVF (or its modified versions) has been applied for cardiac image segmentation (e.g., [12] [13]), but all of the attempts are performed on the LV. Recently, the mean shift based GVF model was proposed for biomedical image segmentation [14] [15], where a mean shift operation is incorporated to drive the internal/external energy towards the desired direction.…”

Section: Introductionmentioning

confidence: 99%

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Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

Yang

Yeo

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Evaluation of right ventricular (RV) structure and function is of importance in the management of most cardiac disorders. But the segmentation of RV has always been considered challenging due to low contrast of the myocardium with surrounding and high shape variability of the RV. In this paper, we present a 2D + T active contour model for segmentation and tracking of RV endocardium on cardiac magnetic resonance (MR) images. To take into account the temporal information between adjacent frames, we propose to integrate the time-dependent constraints into the energy functional of the classical gradient vector flow (GVF). As a result, the prior motion knowledge of RV is introduced in the deformation process through the time-dependent constraints in the proposed GVF-T model. A weighting parameter is introduced to adjust the weight of the temporal information against the image data itself. The additional external edge forces retrieved from the temporal constraints may be useful for the RV segmentation, such that lead to a better segmentation performance. The effectiveness of the proposed approach is supported by experimental results on synthetic and cardiac MR images.

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Section: Resultsmentioning

confidence: 81%

“…To include (13) into the diffusion of the gradient vector flow, we formulate the energy function of GVF-T by…”

Section: B Gvfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

Yang

Yeo

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…The other is necessary to use some training datasets, and is called segmentation with strong prior [8]. The examples of segmentation algorithms are thresholding [8,9], region growing [10], dynamic programming (DP) [11], deformable models [12], graph cuts [12], active contour models (ACM) [13][14], level-set [15], KNN classifier [16], convex relaxed distribution matching [17], robust adaptive Gaussian regularizing Chan-Vese (CV) model [18], and clustering [19]. Most of the previous works were focused on automatic LV segmentation for evaluating the cardiac function, but only a few works were focused on cardiac T2* estimation.…”

Section: Introductionmentioning

confidence: 99%

“…There are many previous works proposed as the methods for automatic segmentation in MR images which focus on different organs such as brain, kidneys, liver, and heart [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Fully automatic cardiac T2* relaxation time estimation using marker-controlled watershed

Wantanajittikul

Saekho

Phrommintikul

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Heart failure due to iron-overload cardiomyopathy is one of the main causes of mortality. The cardiomyopathy is reversible if intensive iron chelation treatment is done in time. However, the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected. The widely used method in many countries is by calculating a parameter called the T2* (T2-star) from magnetic resonance (MR) image sequences. In order to compute the T2* value, the region of interest (ROI) is manually selected by an expert which may require considerable time and skills. The aim of this work is hence to develop the fully automatic cardiac T2* measurement by using marker-controlled watershed for segmenting the interventricular septum in cardiac MR images. Mathematical morphologies are also used to reduce some errors. Moreover, a new approach for T2* evaluation is suggested in this work. The interventricular septa from MR images in all echo times (TE's) are segmented to calculate the signal intensities (SI's) while the classical method segments the interventricular septum only in the first TE. Thirty cardiac MR images were used in this work. The segmentation performances are evaluated only from the MR images in the first TE. The precision and recall are 0.9 and 0.8 compared with the two experts' opinions. It shows that the segmentation results of our proposed technique are quite similar to the experts' opinions. The T2* values were carried out based on the automatically segmented ROI's. The mean difference of T2* values between the proposed method and the experts' opinions is about 1 ms. This demonstrates that the T2* values from the proposed technique are also close to T2* values calculated by the experts.

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Functional Endoscopic Sinus Surgery with Head Mounted Display and Video Analysis

Przybyło

Dobosz

2017

Recent Developments and Achievements in Biocybernetics and Biomedical Engineering

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Segmentation of the Left Ventricle Using Active Contour Method with Gradient Vector Flow Forces in Short-Axis MRI

Cited by 8 publications

References 16 publications

Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

Fully automatic cardiac T2* relaxation time estimation using marker-controlled watershed

Functional Endoscopic Sinus Surgery with Head Mounted Display and Video Analysis

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