Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients
receiving radiotherapy as part of their treatment. The delineation of gross tumour volume
(GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be
beneficial in radiotherapy planning in order to generate objective, accurate and reproducible
GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment
delivery to allow any necessary positional correction. Consequently, real-time contouring
system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely
deformable models (FDM) and shape constrained deformable models (SCDMs) were used to
automatically delineate the GTV for brain cancer and prostate cancer.
Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A
series of low level image segmentation methodologies are cascaded to form a case-wise fully
automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs)
were used to evaluate the contours. Results shown a good agreement between clinical contours
and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%.
The second significant contribution is a novel development to the active shape model (ASM), a
profile feature was selected from pre-computed texture features by minimising the Mahalanobis
distance (MD) to obtain the most distinct feature for each landmark, instead of conventional
image intensity. A new group-wise registration scheme was applied to solve the correspondence
definition within the training data. This ASM model was used to delineated prostate GTV on
CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81.
The last contribution is a fully automatic active appearance model (AAM) which captures
image appearance near the GTV boundary. The image appearance of inner GTV was discarded
to spare the potential disruption caused by brachytherapy seeds or gold markers. This model
outperforms conventional AAM at the prostate base and apex region by involving surround
organs. The overall mean DSC for this case is 0.85
