The paper addresses data processing support that is required in capsule gastrointestinal endoscopy. First, capsule position estimation method using standard MPEG-7 image features (descriptors) is discussed. The proposed approach makes use of vector quantization, principal component analysis and neural networks. Next, new algorithms dedicated for virtual colonoscopy (VC) human body inspection are described. The VC images can be registered with endoscopic ones and help in capsule localization and navigation. Finally, an original, low-complexity, efficient image compression method, based on integer-to-integer 4x4 DCT transform, is presented and experimentally verified.
The paper addresses the problem of very fast real-time estimation of forward and backward broncho-fiberoscope egomotion during the medical procedure of transbronchial biopsy. The proposed algorithm significantly differs from standard egomotion routines since it exploits specific rotational features of the bronchoscopic images resulting from the approximate cylinder-like structure of airway segments. The method is described in detail in the paper and tested on synthetic and real-word endoscopic images. Reported results explicitly confirm its efficiency.
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