We report an in-vitro autofluorescence spectroscopic study of cow eye tissue to explore the applicability of the approach
in discriminating early stage "cancer eye" from normal squamous eye tissues. Significant differences were observed in
the autofluorescence signatures between the "cancer eye" and normal eye tissues. The spectral differences were
quantified by employing a probability-based diagnostic algorithm developed based on recently formulated theory of
Relevance Vector Machine (RVM), a Bayesian machine-learning framework of statistical pattern recognition. The
algorithm provided sensitivity and specificity values of 97 ± 2% towards cancer for the training set data based on leave-one-out cross validation and a sensitivity of 97 ± 2% and a specificity of 99 ± 1% towards cancer for the independent
validation set data. These results suggest that autofluorescence spectroscopy might prove to be a quantitative <i>in-vivo</i>
diagnostic modality for early and accurate diagnosis of "cancer eye" in veterinary clinical setting, which would help
improve ranch management from both economic and animal care standpoint.
Denoising of medical images in wavelet domain has potential application in transmission technologies such as teleradiology. This technique becomes all the more attractive when we consider the progressive transmission in a teleradiology system. The transmitted images are corrupted mainly due to noisy channels. In this paper, we
present a new real time image denoising scheme based on limited restoration of bit-planes of wavelet coefficients. The proposed scheme exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each sub-band. The desired bit-rate control is achieved by applying the restoration on a limited number of bit-planes subject to the optimal smoothing. The proposed method adapts itself to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with unrestored case, in context of error reduction. It also has capability to adapt to situations where noise level in the image varies and with the changing requirements of
medical-experts. The applicability of the proposed approach has implications in restoration of medical images in teleradiology systems. The proposed scheme is computationally efficient.