Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Volumetric analysis of HeLa cancer cells imaged with serial block face scanning electron microscopy

This dissertation investigates the volumetric analysis of a variety of cervical cancer cells called HeLa cells. HeLa cells were derived from cervical cancer cells taken from Henrietta Lacks at the Johns Hopkins Hospital and hence the name HeLa remains. The shape of cells is important as the regular or irregular shape of the cell and its structures can be related to some conditions of health or disease. In this dissertation, a traditional image processing algorithm to segment the nuclear envelope of HeLa cells imaged with Serial Block Face Scanning Electron Microscopy is proposed. The algorithm is fast, robust and accurate and it was compared against different deep learning architectures. Three deep learning architectures were deployed through transfer learning and U-Net was trained from scratch for semantic segmentation of HeLa cells. The algorithm outperformed all four deep learning architectures and active contours (snakes) in both accuracy and time as suggested by the similarity metrics. The segmented nuclear envelope was further investigated through a visualisation technique to obtain a graphical model. This model provides volume and surface metrics which can be used to compare different cells. Wild-type of HeLa cells were compared against Chlamydia trachomatis-infected HeLa cells and geometric differences were revealed. The open-source image processing algorithm, developed in programming environment of Matlab® (The MathworksTM, Natick, USA), provides cell segmentation in a fraction of manual segmentation time therefore it is an alternative to expensive commercial software and manual segmentation, which is still widely used despite the significant disadvantages of time and inter- and intra-user variability