Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical Image Analysis using Convolutional Neural Networks: A Review

Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT

Unsupervised feature selection via latent representation learning and manifold regularization.

Nitrogen-doped carbon dots (NCDs) were synthesised using a simple and straightforward solvothermal method. The resulting spherical NCDs showed stable fluorescence and a prominent fluorescence quantum yield (67%) with an average size of 3.52 nm. The fluorescence of the NCDs was quenched effectively by mercury ions (Hg2+), exhibiting a detection limit of 0.65 μM. Upon addition of a saturated solution of ethylenediaminetetraacetic acid to a dispersion of the NCDs/Hg2+ complex, the fluorescence of the NCDs underwent significant immediate recovery. Due to their favorable biocompatibility, NCDs were also demonstrated to be efficient for HeLa cell imaging. The detection of Hg2+ by NCDs could be realized in environmental water samples and living cells, with excellent results. Moreover, the as-synthesized fluorescent NCD nanopowders were used for visualizating latent fingerprints (LFPs) with significant third level detail.

Tarun Vashisth

Papers

Adaptive latent fingerprint segmentation using feature selection and random decision forest classification