@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 = {<p>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.</p>
},
	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}
}
