Репозиторий Университета

© 2018 Media Sphera Publishing Group. All rights reserved. Forensic medical expertise carried out with a view to reconstruction of an event is a time-consuming procedure because it requires collection of a large amounts of various materials for the institution of a criminal investigation including physical evidence, photoboards of the site of an occurrence, etc. A forensic medical expert may encounter difficulties when reconstructing and scrutinizing the scene of action at a single computer monitor in order to analyze the behaviour of each participant of the event. Of great help in such situations are modern software programs allowing to visualize the site of an occurrence with a maximum approximation to reality, simulate the actions of the victim(s) and alleged offender(s), perform a large number of other forensic studies. The present article provides the practical examples illustrating the possibilities of reconstruction of various events with the use of the three-dimensional modeling based on the MicroSmith Poser and Agisoft PhotoScan software packages for clarifying various circumstances, facts, and conditions of special interest for the preliminary investigation and inquiries.

© 2018 Elsevier Inc. Objectives One of the most reliable methods for diagnosing bladder cancer is cystoscopy. Depending on the findings, this may be followed by a referral to a more experienced urologist or a biopsy and histological analysis of suspicious lesion. In this work, we explore whether computer-assisted triage of cystoscopy findings can identify low-risk lesions and reduce the number of referrals or biopsies, associated complications, and costs, although reducing subjectivity of the procedure and indicating when the risk of a lesion being malignant is minimal. Materials and methods Cystoscopy images taken during routine clinical patient evaluation and supported by biopsy were interpreted by an expert clinician. They were further subjected to an automated image analysis developed to best capture cancer characteristics. The images were transformed and divided into segments, using a specialised color segmentation system. After the selection of a set of highly informative features, the segments were separated into 4 classes: healthy, veins, inflammation, and cancerous. The images were then classified as healthy and diseased, using a linear discriminant, the naïve Bayes, and the quadratic linear classifiers. Performance of the classifiers was measured by using receiver operation characteristic curves. Results The classification system developed here, with the quadratic classifier, yielded 50% false-positive rate and zero false-negative rate, which means, that no malignant lesions would be missed by this classifier. Conclusions Based on criteria used for assessment of cystoscopy images by medical specialists and features that human visual system is less sensitive to, we developed a computer program that carries out automated analysis of cystoscopy images. Our program could be used as a triage to identify patients who do not require referral or further testing.