X-ray film images are the main medical diagnosis tool in the evaluation of the fit of the hip prostheses inserted in total hip arthroplasty (THA) procedures. In a computer-aided diagnosis tool, one of the most important operations is the automatic segmentation of the X-ray image into the clinical relevant parts: prosthesis, bone (femur) and soft tissue. The paper investigates the use of several classical adaptive region segmentation techniques, using either the initial pixel luminance space (adaptive histogram thresholding), or an extended feature space (fuzzy C-means) and evaluates the segmentation quality, by the standard detection error and ROC (receiver operating characteristics) curves.

In this paper we emphasize a similarity between the logarithmic type image processing (LTIP) model and the Naka–Rushton model of the human visual system (HVS). LTIP is a derivation of logarithmic image processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present a unifying framework for the high dynamic range (HDR) imaging problem, namely, that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations