Light transmission data collected around an object show large variation with source-detector separation owing to the presence of single or multiple inhomogeneous regions in the object. This variation in the measured intensity is made use of to reconstruct regions of the inhomogeneous inclusions. In addition, it is possible to select a set of data from the above which is most likely least affected by the presence of the inhomogeneity, and estimate reasonably accurately the background optical properties from it. The reconstructed region is found to always contain the inhomogeneity and is of size approximately 140% by area of the inhomogeneity. With the regions to be reconstructed a priori known, a model-based iterative reconstruction procedure for reconstructing the optical properties of the region converged five times faster than without such information. It is also shown that whereas for the full object, a view-based propagation-backpropagation reconstruction procedure failed to converge, owing to large underdeterminacy of the problem, a smaller problem attempting to reconstruct a priori specified regions of interest converged and did so faster than a non-view-based approach for similar objects. Reconstruction results are presented from simulated transmitted intensity data from the following objects with regions of inhomogeneity in both absorption and scattering: (i) single centrally located inhomogeneity, (ii) two off-centred inhomogeneous regions of equal size and contrast (iii) two off-centred inhomogeneous regions of unequal size and equal contrast and (iv) two off-centred inhomogeneous regions of unequal size and contrast. Whereas the model-based iterative image reconstruction procedure gave good convergence in the first, second and third cases, in the fourth case the reconstructions failed to recover the exact numerical value of the optical properties in the higher contrast region.