Protecting medical images with biometric information

Medical images are private to doctor and patient. Digital medical images should be protected against unauthorized viewers. One way to protect digital medical images is using cryptography to encrypt the images. This paper proposes a method for encrypting medical images with a traditional symmetric cryptosystem. We use biometrics to protect the cryptographic key. Both encrypted image and cryptographic key can be transmitted over public networks with security and only the person that owns the biometrics information used in key protection can decrypt the medical image. © Springer Science+Business Media B.V. 2008.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Imagem digital em movimento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Eliminação de ruído impulsivo em imagens coloridas usando um filtro mediano seletivo e retoque digital

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Subtração digital radiográfica: identificação precoce de perdas minerais em esmalte

Pós-graduação em Biopatologia Bucal - ICT

Desenvolvimento de algoritmos para a avaliação quantitativa de imagens de fantomas mamográficos

There are many advantages when using a phantom for the evaluation of the mammographic images, one of them is to test the visibility of the distribution of objects that constitute it, and then, set the best image in order to obtain a diagnostic medical insurance, trying to reach the best risk - benefit ratio for the patient. Typically, the quality of the mammographic image is performed by subjective and quantitative assessments. This study developed algorithms that can quantitatively evaluate digital images (DICOM), obtained from mammographic phantom consisting of test objects. The results were adjusted by the response of the subjective evaluation performed by experts in radiology. This procedure aims to create an independence of the experts in radiology in daily tests of quality control in routine clinical diagnostic radiology

Time series of ZOOSCAN images and results of zooplankton samples at Villefranche from 1966 to 2003

ZooScan with ZooProcess and Plankton Identifier (PkID) software is an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects. Here we present a semi-automatic approach that entails automated classification of images followed by manual validation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach proposed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplankton. We also evaluate the appropriate number of images to include in digital learning sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa. We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.

Sistemas de ayuda al diagnóstico y reconocimiento de microcalcificaciones en mamografía mediante descriptores de escala y redes jerárquicas

La presente Tesis investiga el campo del reconocimiento automático de imágenes mediante ordenador aplicado al análisis de imágenes médicas en mamografía digital. Hay un interés por desarrollar sistemas de aprendizaje que asistan a los radiólogos en el reconocimiento de las microcalcificaciones para apoyarles en los programas de cribado y prevención del cáncer de mama. Para ello el análisis de las microcalcificaciones se ha revelado como técnica clave de diagnóstico precoz, pero sin embargo el diseño de sistemas automáticos para reconocerlas es complejo por la variabilidad y condiciones de las imágenes mamográficas. En este trabajo se analizan los planteamientos teóricos de diseño de sistemas de reconocimiento de imágenes, con énfasis en los problemas específicos de detección y clasificación de microcalcificaciones. Se ha realizado un estudio que incluye desde las técnicas de operadores morfológicos, redes neuronales, máquinas de vectores soporte, hasta las más recientes de aprendizaje profundo mediante redes neuronales convolucionales, contemplando la importancia de los conceptos de escala y jerarquía a la hora del diseño y sus implicaciones en la búsqueda de la arquitectura de conexiones y capas de la red. Con estos fundamentos teóricos y elementos de diseño procedentes de otros trabajos en este área realizados por el autor, se implementan tres sistemas de reconocimiento de mamografías que reflejan una evolución tecnológica, culminando en un sistema basado en Redes Neuronales Convolucionales (CNN) cuya arquitectura se diseña gracias al análisis teórico anterior y a los resultados prácticos de análisis de escalas llevados a cabo en nuestra base de datos de imágenes. Los tres sistemas se entrenan y validan con la base de datos de mamografías DDSM, con un total de 100 muestras de entrenamiento y 100 de prueba escogidas para evitar sesgos y reflejar fielmente un programa de cribado. La validez de las CNN para el problema que nos ocupa queda demostrada y se propone un camino de investigación para el diseño de su arquitectura. ABSTRACT This Dissertation investigates the field of computer image recognition applied to medical imaging in mammography. There is an interest in developing learning systems to assist radiologists in recognition of microcalcifications to help them in screening programs for prevention of breast cancer. Analysis of microcalcifications has emerged as a key technique for early diagnosis of breast cancer, but the design of automatic systems to recognize them is complicated by the variability and conditions of mammographic images. In this Thesis the theoretical approaches to design image recognition systems are discussed, with emphasis on the specific problems of detection and classification of microcalcifications. Our study includes techniques ranging from morphological operators, neural networks and support vector machines, to the most recent deep convolutional neural networks. We deal with learning theory by analyzing the importance of the concepts of scale and hierarchy at the design stage and its implications in the search for the architecture of connections and network layers. With these theoretical facts and design elements coming from other works in this area done by the author, three mammogram recognition systems which reflect technological developments are implemented, culminating in a system based on Convolutional Neural Networks (CNN), whose architecture is designed thanks to the previously mentioned theoretical study and practical results of analysis conducted on scales in our image database. All three systems are trained and validated against the DDSM mammographic database, with a total of 100 training samples and 100 test samples chosen to avoid bias and stand for a real screening program. The validity of the CNN approach to the problem is demonstrated and a research way to help in designing the architecture of these networks is proposed.

Content-independent embedding scheme for multi-modal medical image watermarking

Background As the increasing adoption of information technology continues to offer better distant medical services, the distribution of, and remote access to digital medical images over public networks continues to grow significantly. Such use of medical images raises serious concerns for their continuous security protection, which digital watermarking has shown great potential to address. Methods We present a content-independent embedding scheme for medical image watermarking. We observe that the perceptual content of medical images varies widely with their modalities. Recent medical image watermarking schemes are image-content dependent and thus they may suffer from inconsistent embedding capacity and visual artefacts. To attain the image content-independent embedding property, we generalise RONI (region of non-interest, to the medical professionals) selection process and use it for embedding by utilising RONI’s least significant bit-planes. The proposed scheme thus avoids the need for RONI segmentation that incurs capacity and computational overheads. Results Our experimental results demonstrate that the proposed embedding scheme performs consistently over a dataset of 370 medical images including their 7 different modalities. Experimental results also verify how the state-of-the-art reversible schemes can have an inconsistent performance for different modalities of medical images. Our scheme has MSSIM (Mean Structural SIMilarity) larger than 0.999 with a deterministically adaptable embedding capacity. Conclusions Our proposed image-content independent embedding scheme is modality-wise consistent, and maintains a good image quality of RONI while keeping all other pixels in the image untouched. Thus, with an appropriate watermarking framework (i.e., with the considerations of watermark generation, embedding and detection functions), our proposed scheme can be viable for the multi-modality medical image applications and distant medical services such as teleradiology and eHealth.

Análise colorimétrica de faces humanas: uma abordagem para auxílio ao reconhecimento de imagens

A quantificação colorimétrica da pele do rosto humano apresenta uma grande dispersão de valores. Esta dispersão varia de acordo com o espaço de cor (HSV ou YCbCr) adotado para a análise e quanto menor a dispersão mais adequado é o espaço ao reconhecimento facial. O objetivo deste trabalho é analisar a distribuição estatística da colorimetria de imagens de rostos digitalizadas. A análise poderá dizer se as coordenadas de cor, tais como saturação, matiz e valor podem auxiliar em técnicas de reconhecimento de faces. Como resultado da análise, espera-se concluir qual dos sistemas de coordenadas de cor (HSV ou YCbCr) é o mais adequado à aplicações em reconhecimento facial. Os resultados obtidos serão apresentados com fundamentação no design da informação. O grande número de amostras fotográficas disponíveis para análise (530) e o correto equilíbrio de iluminação, contraste e temperatura de cor constituem o principal diferencial desse trabalho.

A survey of deep-water coral and sponge habitats along the west coast of the US using a remotely operated vehicle: NOAA Fisheries Survey Vessel (FSV)'Bell M. Shimada', November 1-5, 2010

Remotely operated vehicle (ROV) surveys were conducted from NOAA’s state-of-the-art Fisheries Survey Vessel (FSV) Bell M. Shimada during a six-day transit November 1-5, 2010 between San Diego, CA and Seattle, WA. The objective of this survey was to locate and characterize deep-sea coral and sponge ecosystems at several recommended sites in support of NOAA’s Coral Reef Conservation Program. Deep-sea corals and sponges were photographed and collected whenever possible using the Southwest Fisheries Science Center’s (SWFSC) Phantom ROV ‘Sebastes’ (Fig. 1). The surveyed sites were recommended by National Marine Sanctuary (NMS) scientists at Monterey Bay NMS, Gulf of the Farallones NMS, and Olympic Coast NMS (Fig. 2). The specific sites were: Sur Canyon, The Football, Coquille Bank, and Olympic Coast NMS. During each dive, the ROV collected digital still images, video, navigation, and along-track conductivity-temperature-depth (CTD), and optode data. Video and high-resolution photographs were used to quantify abundance of corals, sponges, and associated fishes and invertebrates to the lowest practicable taxonomic level, and also to classify the seabed by substrate type. A reference laser system was used to quantify area searched and estimate the density of benthic fauna.

Computer technology in detection and staging of prostate carcinoma: a review

Y. Zhu, S. Williams and R. Zwiggelaar, 'Computer technology in detection and staging of prostate carcinoma: a review', Medical Image Analysis 10 (2), 178-199 (2006)

Two-Dimensional?Three-Dimensional Correspondence in Mammography

R. Marti, C. Rubin, E. Denton and R. Zwiggelaar, '2D-3D correspondence in mammography', Cybernetics and Systems 35 (1), 85-105 (2004)

Automatic point correspondence and registration based on linear structures

R. Marti, R. Zwiggelaar, C.M.E. Rubin, 'Automatic point correspondence and registration based on linear structures', International Journal of Pattern Recognition and Artificial Intelligence 16 (3), 331-340 (2002)

Temporalité et surveillance dans l'art de David Rokeby

Pour respecter les droits d'auteur, la version électronique de ce mémoire à été dépouillée de ses documents visuels et audio-visuels. La version intégrale du mémoire a été déposée au Service de la gestion des documents et des archives de l'Université de Montréal.