Medical ultrasonic B-scans often suffer from intensity inhomogeneities that originates from the nonuniform attenuation properties of the sonic beam within the body. In order to correct signal attenuation in the tissue, time gain compensation (TGC) is routinely used in medical ultrasound scanners. However, TGC assumes a uniform attenuation coefficient for all body tissues. Since this assumption is evidently inaccurate, over-amplification or under-amplification sometimes appear. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. The main contribution of this paper is that additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic besides most commonly used intensity features, so that local intensity variations can be corrected more efficiently. The degraded image is corrected by the inverse of the image degradation model. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. Spatial information about intensity nonuniformity is obtained using cubic spline smoothing and entropy minimizing. Gray-level histogram information of the image corrupted by intensity inhomogeneity is exploited from a signal processing perspective. We explain how this model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and real US data to evaluate quantitatively the accuracy of the method.