Combined information from Raman spectroscopy and optical coherence tomography for enhanced diagnostic accuracy in tissue discrimination

We thank the UK EPSRC for funding, the CR-UK/EPSRC/MRC/DoH (England) imaging programme, the European Union project FAMOS (FP7 ICT, contract no. 317744) and the European Union project IIIOS (FP7/2007-2013, contract no. 238802). We thank Tayside Tissue Bank for providing us with the tissue samples under request number TR000289. K.D. is a Royal Society-Wolfson Merit Award Holder.Optical spectroscopy and imaging methods have proved to have potential to discriminate between normal and abnormal tissue types through minimally invasive procedures. Raman spectroscopy and Optical Coherence Tomography (OCT) provides chemical and morphological information of tissues respectively, which are complementary to each other. When used individually they might not be able to obtain high enough sensitivity and specificity that is clinically relevant. In this study we combined Raman spectroscopy information with information obtained from OCT to enhance the sensitivity and specificity in discriminating between Colonic Adenocarcinoma from Normal Colon. OCT being an imaging technique, the information from this technique is conventionally analyzed qualitatively. To combine with Raman spectroscopy information, it was essential to quantify the morphological information obtained from OCT. Texture analysis was used to extract information from OCT images, which in-turn was combined with the information obtained from Raman spectroscopy. The sensitivity and specificity of the classifier was estimated using leave one out cross validation (LOOCV) method where support vector machine (SVM) was used for binary classification of the tissues. The sensitivity obtained using Raman spectroscopy and OCT individually was 89% and 78% respectively and the specificity was 77% and 74% respectively. Combining the information derived using the two techniques increased both sensitivity and specificity to 94% demonstrating that combining complementary optical information enhances diagnostic accuracy. These results demonstrate that a multimodal approach using Raman-OCT would be able to enhance the diagnostic accuracy for identifying normal and cancerous tissue types.Publisher PD

Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

Optical Coherence Tomography and Its Non-medical Applications

Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Remote Extraction of Latent Fingerprints (RELF)

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordLatent fingerprints are the kind left on objects after direct contact with a person’s finger, often unwittingly at crime scenes. Most current techniques for extracting these types of fingerprint are invasive and involve contaminating the fingerprint with chemicals which often renders the fingerprint unusable for further forensic testing. We propose a novel and robust method for extracting latent fingerprints from surfaces without the addition of contaminants or chemicals to the evidence. We show our technique works on notoriously difficult to image surfaces, using off-the-shelf cameras and statistical analysis. In particular, we extract images of latent fingerprints from surfaces which are transparent, curved and specular such as glass lightbulbs and jars, which are challenging due to the curvature of the surface. Our method produces results comparable to more invasive methods and leaves the fingerprint sample unaffected for further forensic analysis. Our technique uses machine learning to identify partial fingerprints between successive images and mosaics them

Biometric identification with 3D fingerprints acquired through optical coherence tomography

Orientador : Prof. Dr. Luciano SilvaCoorientador : Profª. Olga Regina Pereira BellonTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 28/06/2016Inclui referências : f. 75-82Área de concentraçãoResumo: Um método para se obter impressões digitais 3D da derme e da epiderme a partir de imagens em alta resolução adquiridas utilizando Tomografia de Coerência Ótica (OCT) é proposto neste trabalho. Este método, resolve limitações das técnicas de reconstrução 3D de impressões digitais que empregam múltiplas câmeras/triangulação ou iluminação estruturada, tais como variações de resolução do centro para as bordas das impressões digitais 3D causadas por erros de reconstrução, sensibilidade a baixa iluminação e contraste insuficiente. Uma técnica de busca e identificação baseados em padrões inovativos, os "mapas KH " (usados para a segmentação de regiões de superfície em imagens de intensidade e de profundidade), extraídos computando as curvaturas Gaussiana (K) e média (H) de uma região de interesse na vizinhança das minúcias (denominada nuvem de minúcia), é apresentada. Grandes bases de mapas KH, uma para cada nuvem de minúcia identificada, podem ser construídos com essa técnica. A estratégia de busca e identificação, em duas etapas, baseia-se primeiro em padrões locais de gradientes (LGP) dos mapas KH, para reduzir o espaço de busca dentro da base, seguidos de uma comparação que utiliza uma medida de similaridade, a correlação cruzada normalizada dos padrões pré-selecionados com o LGP com os que se quer identificar. A acuracidade do método e sua compatibilidade com os métodos correntes, comparável ou superior à dos métodos 2D, é verificada através da identificação biométrica de impressões digitais 3D utilizando duas bases de imagens, uma adquirida através da tecnologia OCT e a outra gentilmente cedida pela Universidade Politécnica de Hong Kong. A base de imagens OCT, a primeira adquirida com essa tecnologia, é composta de imagens coletadas de onze voluntários em duas sessões de escaneamento e contém imagens de dedos de pessoas com diferentes idades, gênero e etnias e contém casos de cicatrizes, calos e alterações, tais como abrasão e arranhões. Uma base de impressões digitais 2D, obtida dos mesmos voluntários através de um leitor regular de impressões digitais, foi adquirida para permitir uma comparação da técnica proposta com os métodos de identificação tradicionais. A aplicabilidade do método proposto à identificação de impressões digitais alteradas, deterioradas acidentalmente ou intencionalmente, é investigada. Nesses casos, a impressão digital 3D extraída da derme e compatível com a da epiderme é empregada. A identificação destas impressões 3D alteradas é testada utilizando a base de imagens adquiridas com OCT. A acuracidade da técnica é comparada com a obtida utilizando os métodos tradicionais 2D usando os gráficos de taxas de Falsa Aceitação e Falsa Rejeição (FAXxFRR) e de Características Cumulativas de Identificação (CMC). Impressões digitais 2D, extraídas a partir das impressões digitais 3D simulando o rolamento do dedo durante a aquisição (rolamento virtual), foram geradas e sua compatibilidade com as bases de imagens 2D foi testada. Um conjunto de medidas de avaliação de qualidade foram aplicados às bases de imagens de impressões digitais 3D e sua correspondência aos escores de identificação foi analisada para determinar aqueles que podem contribuir para melhorar a acuracidade da identificação. Palavras-chave: Impressões digitais 3D. Identificação Biométrica. Tomografia de Coerência Ótica.Abstract: A method to obtain epidermal and dermal 3D fingerprints from high-resolution images acquired using Optical Coherence Tomography (OCT) is proposed. This method addresses limitations of current 3D reconstruction techniques that employ multiple cameras/triangulation or structured illumination such as depth and resolution variations from the center to the borders of the fingerprint caused by reconstruction errors, sensitivity to low illumination and poor contrast. The availability of these 3D fingerprints allowed the creation of new matching methods that benefit from the rich information available in 3D. A 3D fingerprint matching technique based on novel patterns, the KH maps (used to surface region segmentation in range and intensity images), extracted by computing the Gaussian and mean curvatures (SILVA; BELLON; GOTARDO, 2001) from a region of interest around the minutiae, named minutiae clouds is presented. Large databases of KH maps, one for each identified minutiae cloud can be built. The matching strategy, a two-step approach, relies on local gradient patterns (LGP) of the KH maps to narrow the search space, followed by a similarity matching, the normalized cross correlation of patterns being matched. The accuracy and matching compatibility, comparable or improved in relation to the 2D matching methods, is verified through matching 3D fingerprints from two databases one acquired using OCT and a public database gently made available by the Hong Kong Polytechnic University. The OCT database, the first 3D database acquired using Optical Coherence Tomography, to our knowledge, is made of images collected from eleven volunteers in two scanning sessions and contains images of people of different ages, genders and ethnicities and also cases of scars, calluses and alterations as abrasion and scratches. A 2D fingerprint database, scanned from the same volunteers using a regular fingerprint reader was also obtained for comparison with traditional matching methods. We investigate the applicability of our method to the identification of altered fingerprints, damaged unintentionally or accidentally. In these cases, the 3D dermal fingerprint, compatible with the epidermis fingerprint, is employed. Matching with 3D dermal and epidermal fingerprints is tested in the OCT database. Matching accuracy is compared with the obtained using traditional matching 2D methods by using False Acceptance and False rejection rate (FARxFRR) and Cumulative Matching Characteristics (CMC) graphs. Unwrapped fingerprints, 2D fingerprints extracted from 3D fingerprints by virtual unrolling were generated and tested for compatibility with 2D databases. A set of quality evaluation measures were employed to the 3D fingerprint databases and their correspondence to the matching scores was analyzed to identify those that can contribute to improve the matching accuracy. Key-words: 3D Fingerprints. Biometric identification. Optical Coherence Tomography

Emerging Optical Methods for Endoscopic Barrett’s Surveillance

Barrett’s oesophagus is an acquired metaplastic condition that predisposes patients to the development of oesophageal adenocarcinoma, prompting the use of surveillance regimes to detect early malignancy for endoscopic therapy with curative intent. The currently accepted surveillance regime uses white light endoscopy together with random biopsies, but suffers poor sensitivity and discards information from numerous light-tissue interactions that could be exploited to probe structural, functional and molecular changes in the tissue. Advanced optical methods are now emerging that are exquisitely sensitive to these changes and hold significant potential to improve surveillance of Barrett’s oesophagus if they can be applied endoscopically. The next decade will see some of these exciting new methods applied to Barrett’s surveillance in new device architectures for the first time, potentially leading to a longawaited improvement of the standard of care