Automatic selection of optimal window size and shape for texture analysis

This thesis is a theoretical and empirical examination of the relationship between texture and scale, and its effects on image classification. This study involved creating a model that automatically selected windows of optimal size according to the location of a pixel within a land cover region and the texture of the surrounding pixels. Large windows were used to get a representative sample of within-class variability in the interior of these regions. Smaller windows were used near the boundaries of land cover regions in order to reduce edge effect errors due to between-class variability. This program was tested using a Maximum Likelihood classification scheme against spectral data and texture from fixed-size windows to determine if there were any improvements in classification accuracy. Three different types and scales of data, including SPOT, SIR-C, and ADAR, were used to test the robustness of this program.;The results from this research indicate that the addition of texture can improve classification accuracy, especially in land cover regions with high local variability among the pixels. The 21 x 21 texture image achieved a Kappa Index of Agreement (KIA) of 0.97 for the highly textured Sunlit Forest (leaf off) class in the ADAR data, compared to 0.92 using the spectral data alone. However, texture windows of fixed-size created some errors due to between-class texture. This was most evident in the SPOT interior test data where the 21 x 21 texture window achieved a KIA of 0.70, compared to 0.92 for the spectral data. In many cases, images that incorporated the Optimal Size Window Program were superior in accuracy to all of the other images. In the radar data, the image created from the Optimal Size Window Program improved the overall KIA from 0.51 in the spectral data, to 0.71

Textural features for bladder cancer definition on CT images

Genitourinary cancer refers to the presence of tumours in the genital or urinary organs such as bladder, kidney and prostate. In 2008 the worldwide incidence of bladder cancer was 382,600 with a mortality of 150,282. Radiotherapy is one of the main treatment choices for genitourinary cancer where accurate delineation of the gross tumour volume (GTV) on computed tomography (CT) images is crucial for the success of this treatment. Limited CT resolution and contrast in soft tissue organs make this difficult and has led to significant inter- and intra- clinical variability in defining the extent of the GTV, especially at the junctions of different organs. In addition the introduction of new imaging techniques and modalities has significantly increased the number of the medical images that require contouring. More advanced image processing is required to help reduce contouring variability and assist in handling the increased volume of data. In this thesis image analysis methodologies were used to extract low-level features such as entropy, moment and correlation from radiotherapy planning CT images. These distinctive features were identified and used for defining the GTV and to implement a fully-automatic contouring system. The first key contribution is to demonstrate that second-order statistics from co-occurrence matrices (GTSDM) give higher accuracy in classifying soft tissue regions of interest (ROIs) into GTV and non-GTV. Loadings of the principal components (PCs) of the GTSDM features were found to be consistent over different patients. Exhaustive feature selection suggested that entropies and correlations produced consistently larger areas under receiver operating characteristic (AUROC) curves than first-order features. The second significant contribution is to demonstrate that in the bladder-prostate junction, where the largest inter-clinical variability is observed, the second-order principal entropy from stationery wavelet denoised CT images (DPE) increased the saliency of the bladder prostate junction. As a result thresholding of the DPE produced good agreement between gold standard clinical contours and those produced by this approach with Dice coefficients. The third contribution is to implement a fully automatic and reproducible system for bladder cancer GTV auto-contouring based on classifying second-order statistics. The Dice similarity coefficients (DSCs) were employed to evaluate the automatic contours. It was found that in the mid-range of the bladder the automatic contours are accurate, but in the inferior and superior ends of bladder automatic contours were more likely to have small DSCs with clinical contours, which reconcile with the fact of clinical variability in defining GTVs. A novel male bladder probability atlas was constructed based on the clinical contours and volume estimation from the classification results. Registration of the classification results with this probabilistic atlas consistently increases the DSCs of the inferior slices