Image segmentation in the presence of uncertainty

Keller, James M.; Carpenter, Carl

doi:10.1002/int.4550050205

Cited by 30 publications

(6 citation statements)

References 11 publications

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“…Moreover, even in the case of well-contrasted images, many of the presented methods are limited by the smooth variation of contrast between blood and the myocardium, which makes the contour uncertain. (Keller and Carpenter, 1990). Contrasting with many of the aforementioned processing hypotheses, many pixels belong to several tissues as a consequence of the partial volume effect.…”

Section: Discussionmentioning

confidence: 99%

Left Ventricular Ejection Fraction Calculation from Automatically Selected and Processed Diastolic and Systolic Frames in Short?Axis Cine?MRI

Lalande¹,

Salve²,

Comte³

et al. 2004

J Cardiovasc Magn Reson

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The calculation of the left ventricular ejection fraction (LVEF) is dependent upon the accurate measurement of diastolic and systolic left ventricular volumes. Although breath-hold cine magnetic resonance imaging (MRI) allows coverage of the whole cardiac cycle with an excellent time resolution, many authors rely on the visual selection of diastolic and the systolic short-axis slices in order to reduce the postprocessing time. An automatic method was developed to detect the endocardial contour on each image, allowing an automatic selection of the systolic frame. The calculated ejection fraction was compared with radionuclide ventriculography (RNV). Sixty-five patients were examined using an electrocardiogram (ECG)-gated gradient echo sequence. Among these examinations, manual and automatic processing with MRI were compared when the time of the systolic frame concorded. Good correlations have been found between the automatic MRI approach and RNV, and between manual and automatic processing on MRI alone. The results show that the automatic determination of the ejection fraction is feasible, and should constitute an important step toward a larger acceptance of MRI as a routine tool in heart disease imaging. One major benefit of using automatic postprocessing is that it may eliminate the visual choice of the systolic frame, inaccurate in more than 50% of the studied patients.

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Section: Discussionmentioning

confidence: 99%

Left Ventricular Ejection Fraction Calculation from Automatically Selected and Processed Diastolic and Systolic Frames in Short?Axis Cine?MRI

Lalande¹,

Salve²,

Comte³

et al. 2004

J Cardiovasc Magn Reson

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show abstract

“…This realization motivated the researchers to develop fuzzy image processing and recognition systems [2,9,20,38,52,53]. A gray tone image possesses ambiguity within each pixel because of the possible multi-valued levels of brightness.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Ghosh¹,

Mishra²,

Ghosh³

2011

Information Sciences

289

129

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“…Quantitative analysis and qualitative evaluation can be done by applying fuzzy pattern recognition method. Fuzzy operators, mathematics and inference rules are used in image segmentation [12,13,14,15,16]. In Fuzzy set approach, each pixel in an image has a degree to which it belongs to a region or a boundary, characterized by membership value [4].…”

Section: Fuzzy Approachmentioning

confidence: 99%

Image Segmentation Algorithms on MR Brain Images

Suji¹,

Lakshimi²,

Jiji³

2013

IJCA

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Magnetic Resonance Image plays a major role in medical diagnostics. Image segmentation is done to divide an image into meaningful structures. Image segmentation is the initial step in image analysis and pattern recognition. It becomes more important while dealing with medical images where presurgery and post-surgery decisions are required for the purpose of initiating and speeding up the recovery process. Manual segmentation of abnormal tissues cannot be compared with modern day's high speed computing machines. Segmentation is done to extract the features of the image that are used for analysis, interpretation, and understanding of images. Accuracy of the extracted features decides the accuracy of the algorithm. Selection of a suitable algorithm is highly based on the application. This paper highlights the various image segmentation algorithms, used in medical images.

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Image segmentation in the presence of uncertainty

Cited by 30 publications

References 11 publications

Left Ventricular Ejection Fraction Calculation from Automatically Selected and Processed Diastolic and Systolic Frames in Short?Axis Cine?MRI

Left Ventricular Ejection Fraction Calculation from Automatically Selected and Processed Diastolic and Systolic Frames in Short?Axis Cine?MRI

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Image Segmentation Algorithms on MR Brain Images

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