Binning strategies have been used in much research work for image compression, feature extraction, classification, segmentation and other tasks, but rarely is there any rigorous investigation into which binning strategy is the best. Binning becomes a "hidden parameter" of the research method. This work rigorously investigates the results of three different binning strategies, linear binning, clipped binning, and nonlinear binning, for co-occurrence texture-based classification of the backbone, liver, heart, renal, and splenic parenchyma in high-resolution DICOM Computed Tomography (CT) images of the human chest and abdomen. Linear binning divides the gray-level range of [0..4095] into <i>k1</i> equally sized bins, while clipped binning allocates one large bin for low intensity gray-levels [0..855] (air), one for higher intensities [1368..4095] (bone), and <i>k2</i> equally sized bins for the soft tissues between [856..1368]. Nonlinear binning divides the gray-level range of [0..4095] into <i>k3</i> bins of different sizes. These bins are further used to calculate the co-occurrence statistical model and its ten Haralick descriptors for texture quantification of gray-level images. The results of the texture quantification using each one of the three strategies and for different values of <i>k1</i>, <i>k2</i> and <i>k3</i> are evaluated with respect to their discrimination power using a decision tree classification algorithm and four classification performance metrics (sensitivity, specificity, precision and accuracy). Our preliminary results obtained on 1368 segmented DICOM images show that the optimal number of gray-levels is equal to 128 for linear binning, 512 for clipped binning, , and 256 for non-linear binning. Furthermore, when comparing the results of the three approaches, the nonlinear binning approach shows significant improvement for heart and spleen.