@article {801, title = {Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework}, journal = {IEEE Access}, volume = {3}, year = {2015}, chapter = {2531}, abstract = {

The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection of prostate tumors, since it provides a plethora of mineable data, which can be used for both detection and prognosis of prostate cancer. While current voxel-resolution radiomics-driven prostate tumor detection approaches utilize quantitative radiomics features associated with individual voxels on an independent basis, the incorporation of additional information regarding the spatial and radiomics feature relationships between voxels has significant potential for achieving a more reliable detection performance. Motivated by this, we present a novel approach for automatic prostate cancer detection using a radiomics-driven conditional random field (RD-CRF) framework. In addition to the high-throughput extraction and utilization of a comprehensive set of voxel-level quantitative radiomics features, the proposed RD-CRF framework leverages inter-voxel spatial and radiomics feature relationships to ensure that the autodetected tumor candidates exhibit interconnected tissue characteristics reflective of cancerous tumors. We evaluated the performance of the proposed framework using clinical prostate MP-MRI data of 20 patients, and the results of RD-CRF framework\  demonstrated a clear improvement with respect to the state-of-the-art in quantitative radiomics for automatic voxel-resolution prostate cancer detection.

}, keywords = {Automatic prostate cancer detection, conditional random fields (CRF), feature model, multi-parametric magnetic resonance imaging (MP-MRI), radiomics}, doi = {10.1109/ACCESS.2015.2502220}, author = {A Chung and F Khalvati and M J. Shafiee and M A. Haider and Alexander Wong} } @inproceedings {59, title = {Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection}, booktitle = {International Conference on Image Analysis and Recognition (ICIAR)}, year = {2015}, month = {07/2015}, abstract = {

Prostate cancer is the most diagnosed form of cancer, but survival rates are relatively high with sufficiently early diagnosis. Current computer-aided image-based cancer detection methods face notable challenges include noise in MRI images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. We propose a novel saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness. In this approach, a sparse texture model is learned via expectation maximization from features derived from multi-parametric MR prostate images, and the statistical texture distinctiveness-based saliency based on this model is used to identify suspicious regions. The proposed method was evaluated using real clinical prostate MRI data, and results demonstrate a clear improvement in suspicious region detection relative to the state-of-art method.

}, keywords = {Computer-aided prostate cancer detection, multi-parametric magnetic resonance imaging (MP-MRI), statistical textural distinctiveness, texture-based saliency}, author = {A Chung and C Scharfenberger and F Khalvati and A Wong and M A. Haider} }