UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Perpustakaan Tuanku Bainun |
| Ultrasound-based fetal biometry is used to derive important clinical information for
identifying IUGR (intra-uterine growth restriction) and managing risk in pregnancy. Accurate
and reproducible biometric measurement relies heavily on a good standard image plane.
However, qualitative visual assessment, which includes the visual identification of certain
anatomical landmarks in the image is prone to inter- and intra-reviewer variability and is also
time-consuming to perform. Automated anatomical structure detection is the first step towards the
development of a fast and reproducible quality assessment of fetal biometry images. This thesis
deals specifically with abdominal scans in the development and evaluation of methods to
automatically detect the stomach and the umbilical vem within them.
First, an original method for detecting the stomach and the umbilical vem m fetal
abdominal scans was developed using a machine learning framework. A classifier solution
was designed with AdaBoost learning algorithm with Haar features extracted from the
intensity image. The performance of the new method was compared on different clinically
relevant gestational age groups.
Speckle and the low contrast nature of ultrasound images motivated the idea of
introducing features extracted from local phase images. Local phase is contrast invariant and
has proven to be useful in other ultrasound image analysis application compared with
intensity. Nevertheless, it has never been implemented in a machine learning environment
before. In our second experiment, local phase features were proven to have higher
discriminative power than intensity features which enabled them to be selected as the first
weak classifiers with large classifier weight.
Third, a novel approach to improving the speed of the detection was developed using a global
feature symmetry map based on local phase to select the candidate locations for the stomach and the
umbilical vein. It was coupled with a local intensity-based classifier to form a "hybrid" detector.
A nine-fold increase in the average computational speed was recorded
along with higher accuracy in the detection of both the anatomical structures.
Quantitative and qualitative evaluations of all the algorithms were presented using 2384 fetal
abdominal images retrieved from the image database study of the Oxford
Ultrasound Quality Control Unit of the INTERGROWTH-21⁵¹ project.
Finally, the "hybrid" detection method was evaluated in two potential application scenarios. The
first application was clinical scoring in which both the computer algorithm and four experts were
asked to record presence or absence of the stomach and the umbilical vein in 400 ultrasound images.
The computer-experts agreement was found to be comparable with the inter-expert agreement. The
second application concerned selecting the standard image plane from 3D abdominal ultrasound
volume. The algorithm was successful in selecting
93.36% of the images plane defined by the expert in 30 ultrasound volume:
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