A two terminal optical signal and image processing p-i-n/p-i-n image and colour sensor

A two terminal optically addressed image processing device based on two stacked sensing/switching p-i-n a-SiC:H diodes is presented. The charge packets are injected optically into the p-i-n sensing photodiode and confined at the illuminated regions changing locally the electrical field profile across the p-i-n switching diode. A red scanner is used for charge readout. The various design parameters and addressing architecture trade-offs are discussed. The influence on the transfer functions of an a-SiC:H sensing absorber optimized for red transmittance and blue collection or of a floating anode in between is analysed. Results show that the thin a-SiC:H sensing absorber confines the readout to the switching diode and filters the light allowing full colour detection at two appropriated voltages. When the floating anode is used the spectral response broadens, allowing B&W image recognition with improved light-to-dark sensitivity. A physical model supports the image and colour recognition process.

Image enhancement for digital radiography

Once in a digital form, a radiographic image may be processed in several ways in order to turn the visualization an act of improved diagnostic value. Practitioners should be aware that, depending on each clinical context, digital image processing techniques are available to help to unveil visual information that is, in fact, carried by the bare digital radiograph and may be otherwise neglected. The range of visual enhancement procedures includes simple techniques that deal with the usual brightness and contrast manipulation up to much more elaborate multi-scale processing that provides customized control over the emphasis given to the relevant finer anatomical details. This chapter is intended to give the reader a practical understanding of image enhancement techniques that might be helpful to improve the visual quality of the digital radiographs and thus to contribute to a more reliable and assertive reporting.

An Interface to Retrieve Personal Memories Using an Iconic Visual Language

Relevant past events can be remembered when visualizing related pictures. The main difficulty is how to find these photos in a large personal collection. Query definition and image annotation are key issues to overcome this problem. The former is relevant due to the diversity of the clues provided by our memory when recovering a past moment and the later because images need to be annotated with information regarding those clues to be retrieved. Consequently, tools to recover past memories should deal carefully with these two tasks. This paper describes a user interface designed to explore pictures from personal memories. Users can query the media collection in several ways and for this reason an iconic visual language to define queries is proposed. Automatic and semi-automatic annotation is also performed using the image content and the audio information obtained when users show their images to others. The paper also presents the user interface evaluation based on tests with 58 participants.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Visual functional analysis in medical imaging

Aims of study: 1) Describe the importance of human visual system on lesion detection in medical imaging perception research; 2) Discuss the relevance of research in medical imaging addressing visual function analysis; 3) Identify visual function tests which could be conducted on observers prior to participation in medical imaging perception research.

Perceção visual na avaliação diagnóstica em mamografia: uma revisão sistemática

Introdução – Na avaliação diagnóstica em mamografia, o desempenho do radiologista pode estar sujeito a erros de diagnóstico. Objetivo – Descrever a importância da perceção visual na análise da mamografia, identificando os principais fatores que contribuem para a perceção visual do radiologista e que condicionam a acuidade diagnóstica. Metodologia – Estudo descritivo baseado numa revisão sistemática de literatura através da PubMed e da Science Direct. Foram incluídos 42 artigos que respeitavam, pelo menos, um dos critérios de inclusão no estudo. Para a seleção das referências foi utilizada a metodologia PRISMA, constituída por 4 fases: identificação, seleção preliminar, elegibilidade e estudos incluídos. Resultados – Na avaliação diagnóstica em mamografia, a perceção visual está intimamente relacionada com: 1) diferentes parâmetros visuais e da motilidade ocular (acuidade visual, sensibilidade ao contraste e à luminância e movimentos oculares); 2) com condições de visualização de uma imagem (iluminância da sala e luminância do monitor); e 3) fadiga ocular provocada pela observação diária consecutiva de imagens. Conclusões – A perceção visual pode ser influenciada por 3 categorias de erros observados: erros de pesquisa (lesões não são fixadas pela fóvea), erros de reconhecimento (lesões fixadas, mas não durante o tempo suficiente) e erros de decisão (lesões fixadas, mas não identificadas como suspeitas). Os estudos analisados sobre perceção visual, atenção visual e estratégia visual, bem como os estudos sobre condições de visualização não caracterizam a função visual dos observadores. Para uma avaliação correta da perceção visual em mamografia deverão ser efetuados estudos que correlacionem a função visual com a qualidade diagnóstica. ABSTRACT - Introduction – Diagnostic evaluation in mammography could be influenced by the radiologist performance that could be under diagnostic errors. Aims – To describe the importance of radiologist visual perception in mammographic diagnostic evaluation and to identify the main factors that contribute to diagnostic accuracy. Methods – In this systematic review 42 references were included based on inclusion criteria (PubMed and Science Direct). PRISMA method was used to select the references following 4 steps: identification, screening, eligibility and included references. Results – Visual perception in mammography diagnostic evaluation is related with: 1) visual parameters and ocular motility (visual acuity, contrast sensitivity and luminance and ocular movements); 2) image visualization environment (room iluminance and monitor luminance); and 3) eyestrain caused by image daily consecutive observation. Conclusions – Visual perception can be influenced by three errors categories: search errors (lesions are never looked at with high-resolution foveal vision), recognition errors (lesions are looked at, but not long enough to detect or recognize) and decision errors (lesions are looked at for long periods of time but are still missed). The reviewed studies concerning visual perception, visual attention, visual strategies and image visualization environment do not describe observer’s visual function. An accurate evaluation of visual perception in mammography must include visual function analysis.

Avaliação de dados funcionais em repouso do cérebro normal: causalidade de Granger

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

Tratamento de cor em pós-produção televisiva: o caso da produtora de conteúdos SP Televisão

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

A denoising approach for iterative side information creation in distributed video coding

In distributed video coding, motion estimation is typically performed at the decoder to generate the side information, increasing the decoder complexity while providing low complexity encoding in comparison with predictive video coding. Motion estimation can be performed once to create the side information or several times to refine the side information quality along the decoding process. In this paper, motion estimation is performed at the decoder side to generate multiple side information hypotheses which are adaptively and dynamically combined, whenever additional decoded information is available. The proposed iterative side information creation algorithm is inspired in video denoising filters and requires some statistics of the virtual channel between each side information hypothesis and the original data. With the proposed denoising algorithm for side information creation, a RD performance gain up to 1.2 dB is obtained for the same bitrate.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.

Comparison of 2.3 & 5 mega pixel (MP) resolution monitors when detecting mammography image blurring

Background - Image blurring in Full Field Digital Mammography (FFDM) is reported to be a problem within many UK breast screening units resulting in significant proportion of technical repeats/recalls. Our study investigates monitors of differing pixel resolution, and whether there is a difference in blurring detection between a 2.3 MP technical review monitor and a 5MP standard reporting monitor. Methods - Simulation software was created to induce different magnitudes of blur on 20 artifact free FFDM screening images. 120 blurred and non-blurred images were randomized and displayed on the 2.3 and 5MP monitors; they were reviewed by 28 trained observers. Monitors were calibrated to the DICOM Grayscale Standard Display Function. T-test was used to determine whether significant differences exist in blurring detection between the monitors. Results - The blurring detection rate on the 2.3MP monitor for 0.2, 0.4, 0.6, 0.8 and 1 mm blur was 46, 59, 66, 77and 78% respectively; and on the 5MP monitor 44, 70, 83 , 96 and 98%. All the non-motion images were identified correctly. A statistical difference (p <0.01) in the blurring detection rate between the two monitors was demonstrated. Conclusions - Given the results of this study and knowing that monitors as low as 1 MP are used in clinical practice, we speculate that technical recall/repeat rates because of blurring could be reduced if higher resolution monitors are used for technical review at the time of imaging. Further work is needed to determine monitor minimum specification for visual blurring detection.

Adaptive deblocking filter for transform domain Wyner-Ziv video coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63 dB in the overall for sequences with high motion content when large group of pictures are used.

Statistical motion learning for improved transform domain Wyner-Ziv video coding

Wyner - Ziv (WZ) video coding is a particular case of distributed video coding (DVC), the recent video coding paradigm based on the Slepian - Wolf and Wyner - Ziv theorems which exploits the source temporal correlation at the decoder and not at the encoder as in predictive video coding. Although some progress has been made in the last years, WZ video coding is still far from the compression performance of predictive video coding, especially for high and complex motion contents. The WZ video codec adopted in this study is based on a transform domain WZ video coding architecture with feedback channel-driven rate control, whose modules have been improved with some recent coding tools. This study proposes a novel motion learning approach to successively improve the rate-distortion (RD) performance of the WZ video codec as the decoding proceeds, making use of the already decoded transform bands to improve the decoding process for the remaining transform bands. The results obtained reveal gains up to 2.3 dB in the RD curves against the performance for the same codec without the proposed motion learning approach for high motion sequences and long group of pictures (GOP) sizes.

Stacked pin Devices for Imaging Applications

In this paper we present results on the optimization of device architectures for colour and imaging applications, using a device with a TCO/pinpi'n/TCO configuration. The effect of the applied voltage on the color selectivity is discussed. Results show that the spectral response curves demonstrate rather good separation between the red, green and blue basic colors. Combining the information obtained under positive and negative applied bias a colour image is acquired without colour filters or pixel architecture. A low level image processing algorithm is used for the colour image reconstruction.