Several concepts have been proposed to improve the spatial resolution of near infrared (NIR) tomography, e.g. CW illumination with spatial collimation, deconvolution, Fourier plane filtering, impulse illumination with temporal discrimination, frequency modulated illumination with amplitude- and phase-sensitive detection,...Another point of major importance is that, for a given technique, the image obtained is due to a specific combination of the local variations of the diffusion and absorption coefficients. This second aspect has been studied much less systematically. Incoherent light transport in tissues can be modeled by the radiative transfer equation. The diffusion approximation is applicable to 'thick enough' tissue and is very useful by the simple analytic solutions it provides. If sources and boundary conditions are treated carefully, the validity of this approximation is already good at modest values of the source- detector distance--except at very early times. We use the diffusion approximation and a perturbation approach. For CW illumination, we quantitatively evaluate the image of a small 'defect' imbedded in a homogeneous tissue, as a function of the characteristics of the defect (position, volume, variations of diffusion, and absorption) and of the geometry (wide or narrow light beam, thickness of the tissue, position of the detector). We show how these results could be used to optimize the discrimination of NIR imaging techniques.