Under the assumption of high scattering and weak absorbing media, diffusion approximation holds in the radiative transport equation to model propagation of light. Diffusion approximation is valid deep inside the medium, not near the boundary. So, we need to implement accurate boundary conditions. Diffuse reflectance close to the source, majorly, depends on the source model inside the medium and boundary conditions used to derive the analytical solution. We have implemented partial current boundary condition and extrapolated boundary condition with extended isotropic source (exponentially decaying) model. Our model predicts diffuse reflectance close to the source at distance less than one mean free path is more accurate than the other methods. Monte-carlo simulation is the standard model to provide diffuse reflectance close to source most accurately. In this report, partial current, extrapolated boundary condition and a unified boundary condition have been compared for accuracy at different regions from the source. It is found that different boundary conditions work in different regimes and the relative error is less with extended source compared to point source.
A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.
We report here a study of confocal microscope images to classify cervical precancers by a multifractal analysis. This study is performed using an inverted confocal microscope with laser scanning fluorescence imaging. The periodic structure of collagen present in the stromal region of cervical tissue gets disordered with progress in grade of dysplasia. This disorder is investigated through the β-exponent of a Discrete Fourier Transform (DFT) of the confocal images, enabling us to discriminate between the lowest and highest grades of dysplasia in human cervical tissue sections. The Holder exponent from 2D images further classifies various grades of dysplasia from normal tissue sections though Gd3 and Gd1 are indistinguishable. DFT however, clearly distinguishes Gd3 from Gd1. In addition to stromal images, epithelial images were also investigated for better classification. The cellular density of epithelium increases with depth for various grades of dysplasia and is not uniform. The Holder exponent, which measures multifractality, is higher for dysplastic tissue sections than for normal ones because of the above morphological differences. Extraction of subtle fluctuations from optical images through multifractal studies promise to be a powerful diagnostic technique.