| Summary: | When geographical distance poses as a barrier, telepathology is designed to offer pathologists the opportunity to replicate their normal activities by using an alternative means of practice. The rapid progression in technology has greatly influenced the appeal of telepathology and its use in multiple domains. To that point, telepathology systems help to afford teleconsultation services for remote locations, improve the workload distribution in clinical environments, measure quality assurance, and also enhance educational programs.
While telepathology is an attractive method to many potential users, the resource requirements for digitizing microscopic specimens have hindered widespread adoption. The use of image compression is extremely critical to help advance the pervasiveness of digital images in pathology. For this research, we characterize two different methods that we use to assess compression of pathology images. Our first method is characterized by the fact that image quality is human-based and completely subjective in terms of interpretation. Our second method is characterized by the fact that image analysis is introduced by using machine-based interpretation to provide objective results. Additionally, the objective outcomes from the image analysis may also be used to help confirm tumor classification. With these two methods in mind, the purpose of this dissertation is to quantify the effects of image compression on data interpretation as seen by human experts and a computerized algorithm for use in telepathology.
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