Fuzzy-based echocardiogram boundary enhancement
In cardiology, ultrasound is rapidly becoming one of the most sought after tool to investigate cardiac performance. Echocardiogram, or cardiac ultrasound, has many advantages and plays an important role in studies concerning heart disease. Nevertheless, speckle noise, poor contrast, and artifact...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/18955/1/Fuzzy-based%20echocardiogram.pdf https://eprints.ums.edu.my/id/eprint/18955/ |
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| Summary: | In cardiology, ultrasound is rapidly becoming one of the most sought
after tool to investigate cardiac performance. Echocardiogram, or
cardiac ultrasound, has many advantages and plays an important role
in studies concerning heart disease. Nevertheless, speckle noise,
poor contrast, and artifacts in echocardiogram hamper human
interpretation and impede automated analysis. The image quality
degradation has rendered echocardiogram boundary enhancement
necessary. However, edges in echocardiogram are identified with
ambiguous spatial and intensity information that pose challenges for
methods that depend on conventional gradient approximations.
Although many other techniques have been proposed to improve
speckle tarnished images, most of them are problem-oriented and
hold certain compromise. Many techniques commonly take tentative
solutions by making trade-off between noise suppression and edge
detection while some others are motivated by statistics and subject to
noise modeling error. Apart from that, there are also techniques
available which require time consuming manual definition and are
liable to observer variability. Therefore, two different approaches
incorporating fuzzy-based methods are proposed to automate the
boundary enhancement process and reduce the commonly
encountered drawbacks aforementioned. The first approach employs
a multiscale scheme to resolve the uncertainty in the information
obtained across scales so as to localize the features at low scales
and minimize noise at higher scales. In the proposed multiscale
analysis, fuzzy reasoning provides a mean to combine the derivative
information of various scales stored in a filter bank. By doing so, the
proposed approach reduces the trade-off between noise suppression
and edge detection that is inherent in fixed scale operators. On the
other hand, the second approach attempts to take advantage of the
highly intuitive and appealing aspect of edge definition in
echocardiogram by manipulating the local image characteristics.
Edges in noise tarnished echocardiogram manifest themselves
differently from the standard definition that typically characterizes
edges in an image as abrupt changes in gray-level. Nevertheless, the
ambiguous edge definition allows the second fuzzy-based approach
to work on different edge notions that are defined by operators based
on local statistics in the image. All the proposed methods are
comprised of a comprehensive series of noise suppression, fuzzy
reasoning and boundary extraction operations. Some of the proposed
boundary enhancement methods are also optimized by a neuro-fuzzy
system with learning capability. The results of the proposed methods
are compared with each other and to that of a conventional method
for performance evaluation. The results are compared subjectively by
visual observation for qualitative performance analysis. In addition,
the results are measured using a standard performance index for
quantitative comparison. |
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