Active appearance models for segmentation of cardiac MRI data

Inamdar, R.; Ramdasi, Dipali

doi:10.1109/iccsp.2013.6577023

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Unlike prior automated algorithms [ 12 , 15 – 23 , 25 – 30 ], this LV-FAST algorithm makes use of the spatiotemporal continuity of the 4D (cine volumetric) LV data to define the apex and base automatically and estimate the LV volume. The LV-FAST method starts with the midventricular slice using the iterative decreasing threshold algorithm, whose accuracy and robustness in clinical use are proven in [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

“…Among existing automated methods [ 14 ], approaches with strong prior knowledge, such as active appearance model [ 15 ], Gaussian-mixture model [ 16 ], random walks [ 17 , 18 ], and graph cuts [ 19 – 21 ], are ineffective for extracting accurate basal LV and excluding details like particular and trabecular mussels from LV. Approaches with weak or no prior knowledge, such as level sets [ 22 ], active contour model [ 23 ], iterative threshold-decreasing region growth [ 24 ] or multiseed region growth [ 25 ], and dynamic programming [ 26 ], typically require manual identification of the basal slices.…”

Section: Introductionmentioning

confidence: 99%

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Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

Wang

Pei

Codella

et al. 2015

BioMed Research International

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CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST). An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within −1.6 ± 8.7 mL, −1.4 ± 7.8 mL, and 1.0 ± 5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

Wang

Pei

Codella

et al. 2015

BioMed Research International

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“…Amongst surviving robotic techniques [25], methodologies with solid former awareness, such as active appearance model [26], Gaussian-mixture model [27], arbitrary strides [28,29], and graph cuts[3032], are in effect for mining precise basal LV and rejecting specifics resembling definite and trabecular muscles from LV. Tactics with fragile or no earlier awareness, such level sets [33], Active contour model [34], iterative threshold-decreasing region growth [35] or multi seed region growth [36], and dynamic programming [37], naturally necessitate manual credentials of the basal slices.…”

Section: Related Workmentioning

confidence: 99%

Inevitable dissection of left ventricle aimed at discovery of cardiac blood flow appraisal

Inbalatha¹,

Kalaivani²

2017

IJET

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Cardiac Magnetic Resonance Images (MRI) are chastely indispensable implement for truthful demarcation of the left Ventricle myocardium for purposeful and serviceable cardiac examination. Dissection of MRI principally the leftward ventricle (LV) is a blistering focus with extraordinary perplexing owed to deceive delineation such as percentages misalignment in its position, in homogeneity of sequential portions and to clinch divergence in focus of adjoining pixels for the change in the body fluid drift. In this effort, we put forward a scheme to inevitably fragment the leftward Ventricle of MRI images by Round Hough Transform (RHT) initially by locating the seed point stalked a circle with apt radius as basal region and verdict the ROI in the image. Adjoining the center, apex region of the left ventricle is positioned till the extent and shape connection obsolete plus the mid region from the intermediate ventricle to the base up to the myocardial fringe. This same images of 21 subjects are compared with the manual delineation method by locating the center manually. Mean and Standard deviation is calculated for the basal, mid and apex regions of the LV for auto and manual segmentation. Bland –Altman investigation is performed for End Diastolic Measurements in volume (EDV) with the existing investigations and found to be more close to the ground truth results and superior than the previously enduring methods. To conclude the projected segmentation process truthfully locates and segments the LV which trials the one hundredth of blood pour. EDV measurements are established with less computational time to find the patient’s heart failure with myocardial Infarction.Objective: To spot the cardiac maladies triggered owed to stumbling block of blood by dissection of left Ventricle with MRI image.

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“…Subsequently, it is automatically deformed to match the landmarks with the strongest edges for object delineation. A similar approach is active appearance models (AAMs), [24][25][26][27][28] which incorporate texture properties. An extensive survey regarding both models can be found in Ref.…”

Section: Preliminary Considerationsmentioning

confidence: 99%

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models

2015

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Although multiple models per object can usually improve segmentation efficacy, the optimum object search has shown to reduce the number of required models. The efficiency gain of FOSM over SOSM-S motivates its use for interactive applications and studies with large image data sets. FOSM and SOSM impose different degrees of shape constraints from the model, making one approach more suitable than the other, depending on contrast. This suggests the use of hybrid models that can take advantage from the strengths of fuzzy and statistical models.

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Active appearance models for segmentation of cardiac MRI data

Cited by 7 publications

References 19 publications

Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

Inevitable dissection of left ventricle aimed at discovery of cardiac blood flow appraisal

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models

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