Many applications can benefit from distributed systems based on multiple computers interconnected by a communication network. Distributed systems use inexpensive high-performance computers and can be configured closely to the application. Information can be replicated on several processors to improve performance and to provide fault tolerance. However, programming distributed applications is difficult, particularly when replicated information must remain consistent as it is updated in the presence of faults. Since many messages may be required, recovery from faults may introduce delays, making real-time performance objectives difficult to achieve.Ordered multicast group communication systems are a useful infrastructure on which complex distributed applications can be built.

The vast majority of bladder cancers originate within 600 microm of the tissue surface, making optical coherence tomography (OCT) a potentially powerful tool for recognizing cancers that are not easily visible with current techniques. OCT is a new technology, however, and surgeons are not familiar with the resulting images. Technology able to analyze and provide diagnoses based on OCT images would improve the clinical utility of OCT systems. We present an automated algorithm that uses texture analysis to detect bladder cancer from OCT images. Our algorithm was applied to 182 OCT images of bladder tissue, taken from 68 distinct areas and 21 patients, to classify the images as noncancerous, dysplasia, carcinoma in situ (CIS), or papillary lesions, and to determine tumor invasion. The results, when compared with the corresponding pathology, indicate that the algorithm is effective at differentiating cancerous from noncancerous tissue with a sensitivity of 92% and a specificity of 62%. With further research to improve discrimination between cancer types and recognition of false positives, it may be possible to use OCT to guide endoscopic biopsies toward tissue likely to contain cancer and to avoid unnecessary biopsies of normal tissue.

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Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.