Automatic Regions of Interest Segmentation for Computer Aided Classification of Prostate Trus Images

A novel NMF-based DWI CAD framework for prostate cancer.

“…From the pixel/voxel intensities, several features can be obtained. For example, Gaussian statistics (mean and standard deviation) of pixel/voxels intensities are used as features in several TRUS studies [151][152][153][154][155]. The Nakagami distribution has also been used for extracting features from pixel intensities in various studies [153,154].…”

“…Examples of these include wavelet coefficients [160] and their polynomial fitting [160], which were used in [153][154][155], the autocorrelation coefficients [151], and a tumor's shape metric [151]. In addition, fractal texture features [161] and spectral features [162] were also used in prostate CAD systems, such as in [155] and [153][154][155], respectively. Another feature utilized is the total least square estimation of signal parameters [157], which is estimated via rotational invariance techniques (TLS-ESPRIT) [163,164].…”

“…Using an LDA classifier, this system had a sensitivity of 75±9%, a specificity of 93±2%, an accuracy of 93±2%, and an AUC of 0.95±0.02. Scebran et al [155] proposed a three step automated CAD system. First, possible tumor ROIs were segmented using a combination of k-means and Bayesian pixel classification.…”