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Dr. Yanong Zhu
Research Fellow
School of Computing
University of Leeds
Tel: +44 (0)113 3437288
Email: 
Welcome to my personal homepage!
I am currently working as a Research Fellow in the School of Computing, University of Leeds. My major research area is computer vision and image processing. I have been working on medical image processing and visualisation techniques in the past ten years. The topic of my current research is "An Ultrasound Simulation System for the Training of Ultrasound Guided Needle Insertion Procedures".
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"An Ultrasound Simulation System for the Training of Ultrasound Guided Needle Insertion Procedures"
Needle placement into a patient body under guidance of ultrasound is a frequently performed procedure in clinical practice. Safe
and successful performance of such procedure requires a high level of spatial reasoning and hand-eye co-ordination skills, which must be developed through intensive practice. In this paper we present a training system designed to improve the skills of interventional radiology trainees
in ultrasound-guided needle placement procedures. Key issues involved
in the system include surface and volumetric registration, solid texture
modelling, spatial calibration, and real-time synthesis and rendering of
ultrasound images. Moreover, soft tissue deformation caused by the needle movement and needle cutting is realised using a mass-spring-model
approach. These have led to a realistic and accurate ultrasound simulation system, and therefore a useful tool for the training of needle insertion
procedures. Preliminary results of a construct evaluation study indicate the effetiveness and usefulness of the developed system.
Figure: The ultrasound simulation system and
examples of synthetic ultrasound images.
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.: Plant Area Measurement
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"Arabidopsis Leaf Measurement"
AraLeafMeasure (ALM, or Arabidopsis Leaf Measurement) is a software specially designed for the purposes of measuring the area of Arabidopsis plants, and performing analysis of sequences of plant images. Click here for a webpage that contains an introduction to the software and its download link.
Figure: Main interface of the ALM software.
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"Automatic Prostate Segmentation and Cancer Staging Using MRI"
As the second leading cause of death from cancer in men, prostate cancer is a significant
health problem in the modern world. This research is aimed at investigating the application
of computer techniques in detection and staging of prostate cancer. The research work can
be divided into two stages. The first is focused on the automatic segmentation of the prostate
from Magnetic Resonance Imaging (MRI) data, and the second covers the identification of
the prostate cancer stage using pattern recognition techniques.
The segmentation of objects of interest from image data is an important and also a difficult
task. We investigate both 2D and volumetric segmentation approaches based on the
Active Shape Modelling (ASM) method. First, to enhance the ability of the ASM method to
deal with complex intensity appearance variations, we investigate the modelling of intensity
variations using Gaussian mixture models instead of a single Gaussian distribution. Moving
to 3D segmentation, we first present a B-spline technique to allow the construction of closed
surfaces from planar contours, and then develop a hybrid 2D+3D ASM method which is
able to perform efficient segmentation of volumetric image data, especially those with significant
inter-slice distances. To demonstrate their performance and efficiency, the developed
methods are applied to both medical and non-medical image data and compared with more
traditional approaches, and improved results are demonstrated in these experiments.
Figure: Segmentation of the prostate using the Hybrid 2D+3D ASM method.
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Journal Publications:
 Y. Zhu, R. Zwiggelaar and S. Williams, "Computer Technology in Detection and Staging of Prostate Carcinoma: A Review", Medical Image Analysis, vol. 10(2), pp. 178-99, April, 2006.
Y. Zhu, R. Zwiggelaar and S. Williams, "A Hybrid ASM Approach for Sparse Volumetric Data Segmentation", Pattern Recognition and Image Analysis, vol. 15(2), pp. 346-349, 2005.
Conference Papers:
Y. Zhu, D. R. Magee, D. Kessel and R. Ratnalingam, "A virtual ultrasound imaging system for the simulation of ultrasound-guided needle insertion procedures", Proceedings of Medical Image Understanding and Analysis, pp. 61-65, 2006.
Y. Zhu, S. Williams, M. Fisher and R. Zwiggelaar, "The Use of Grey-level Profiles
for Detection of Extracapsular Extension of Prostate Cancer from MRI", Proceedings of Medical Image Understanding and Analysis, pp. 215-218, 2005.
Y. Zhu, M. Fisher and R. Zwiggelaar, "Improving ASM Search Using Mixture Models for Grey-Level Profiles", Lecture Notes in Computer Science, vol. 3522, Proceedings of IbPRIA 2005, pp. 292-299, 2005.
A complete list of my publications is here (new window will open).
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Last updated: 19th
May, 2008
©
2002-2008 Yanong Zhu - All rights reserved
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