A new algorithm that can be used to automatically recognize and classify malignant lymphomas and leukemia is proposed in this paper. The algorithm utilizes the morphological watersheds to obtain boundaries of cells from cell images and isolate them from the surrounding background. The areas of cells are extracted from cell images after background subtraction. The Radon transform and higher-order spectra (HOS) analysis are utilized as an image processing tool to generate class feature vectors of different type cells and to extract testing cells' feature vectors. The testing cells' feature vectors are then compared with the known class feature vectors for a possible match by computing the Euclidean distances. The cell in question is classified as belonging to one of the existing cell classes in the least Euclidean distance sense.
Biometric identification relies on information that is difficult to misplace or duplicate, making it a very useful tool when properly implemented. One biometric feature of considerable interest is the iris. Since most people rely heavily on their vision, they are protective of their eyes. This means there is less likelihood of change due to environmental factors. In addition, since the iris is created in a random morphogenetic process, there is a large amount of complexity suitable for use as a discriminator.
There are currently several powerful methods available for using the human iris as a biometric for identification. One drawback inherent in the existing methods, however, is their computational complexity. Adopting stochastic models can provide an approach to reducing the extensive computing burden. To this end, we have presented two methods that rely on a wide-sense stationary approximation to the texture and gray scale information in the iris; one uses auto- and cross-correlations while the other employs second order statistics of co-occurrence matrices. Our experiments indicate that cross- and auto-correlations and co-occurrence matrix features are likely to be prominent iris discriminators for correct identification.
Future tests will be conducted on larger sample sets to further verify the findings presented here. Two main methods for feature generation will also be compared and combined to produce an optimal classification strategy for an embedded hardware realization of the method. The addition of more features for discrimination is a likely necessity for classifying larger numbers of irises.