Endoscopic procedures form part of routine clinical practice for minimally invasive examinations and interventions. While they are beneficial for the patient, reducing surgical trauma and making convalescence times shorter, they make orientation and manipulation more challenging for the physician, due to the limited field of view through the endoscope. However, this drawback can be reduced by means of medical image processing and computer vision, using image stitching and surface reconstruction methods to expand the field of view. This paper provides a comprehensive overview of the current state of the art in endoscopic image stitching and surface reconstruction. The literature in the relevant fields of application and algorithmic approaches is surveyed. The technological maturity of the methods and current challenges and trends are analyzed.

Differential blood count is a standard method in hematological laboratory diagnosis. In the course of developing a computer-assisted microscopy system for the generation of differential blood counts, the detection and segmentation of white and red blood cells forms an essential step and its exactness is a fundamental prerequisite for the effectiveness of the subsequent classification step. We propose a method for the exact segmentation of leukocytes and erythrocytes in a simultaneous and cooperative way. We combine pixel-wise classification with template matching to locate erythrocytes and use a level-set approach in order to get the exact cell contours of leukocyte nucleus and plasma regions as well as erythrocyte regions. An evaluation comparing the performance of the algorithm to the manual segmentation performed by several persons yielded good results.

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In the field of minimally invasive surgery one barrier in clinical practice is the limited field of view provided by endoscopic cameras. We propose an image mosaicking approach to extend the field of view for real-time visualization by stitching several video frames. The approach is based on feature tracking and a robust estimation of the imageto-image transformations. We compare its performance to that of a state-of-the-art approach. Our method shows superior accuracy at frame rates of 6.8 to 8.1 frames per second, which allows for real-time visualization of the extended field of view.

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