We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.

Concentrators are used to interface and combine together low speed communication channels onto higher speed transmission links to alleviate transmission costs. They are also used to construct more powerful switching fabrics such as permutation and broadcast networks. Using an adaptive binary sorting network model, this paper constructs new concentrators and superconcentrators. Unlike the previously reported concentrators and superconcentrators, these new constructions are fast, and ran easily be implemented using simple switching devices. More specifically, for n inputs, they can be constructed with O(n lg lg n) constant fanin bit-level multiplexers and demultiplexers, and can be routed in O(lg/sup 2/ n) bit-level time.>

Many routing problems in parallel processing, such as concentration and permutation problems, can be cast as sorting problems. In this paper, we consider the problem of sorting on a new model, called an adaptive sorting network. We show that any sequence of n bits can be sorted on this model in O(lg/sup 2/ n) bit-level delay using O(n) constant fanin gates. This improves the cost complexity of K.E. Batcher's binary sorters (1968) by a factor of O(lg/sup 2/ n) while matching their sorting time. The only other network that can sort binary sequences in O(n) cost is the network version of columnsort algorithm, but this requires excessive pipelining. In addition, using binary sorters, we construct permutation networks with O(n lg n) bit-level cost and O(lg/sup 3/ n) bit-level delay. These results provide the asymptotically least-cost practical concentrators and permutation networks to date. We note, of course, that the well-known AKS sorting network has O(lg n) sorting time and O(n lg n) cost, but the constants hidden in these complexities are so large that our complexities outperform those of the AKS sorting network until n becomes extremely large.>