Comparative evaluation of active contour model extensions for automated cardiac MR image segmentation by regional error assessment

Nguyen, Duy; Masterson, Karen; Vallée, Jean‐Paul

doi:10.1007/s10334-007-0069-z

Cited by 16 publications

(5 citation statements)

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“…The active contour model can be classified into the edge-based models, which includes parametric models [17,18,19,20,21,22] and the geometric (or geodesic) models [14,23,24,25,26,27], and the region-based models [11,28,29]. This paper focuses on the parametric models and proposes a novel model to segment infrared images more accurately.…”

Section: Introductionmentioning

confidence: 99%

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Zhang

Zhu

Zhou

2016

Sensors

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Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models.

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Section: Introductionmentioning

confidence: 99%

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Zhang

Zhu

Zhou

2016

Sensors

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“…However, the endocardial contour is not smooth enough and the movement constraint based on image intensity for the snake is too empirical. Nguyen et al [25] compared the conventional snake, balloon snake and GVF snake on extracting the LV endocardium and concluded that the GVF snake has the best performance.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

Wang

Jia

2013

Computer Vision and Image Understanding

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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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Comparative evaluation of active contour model extensions for automated cardiac MR image segmentation by regional error assessment

Cited by 16 publications

References 50 publications

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model

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

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