João Miguel dos Santos

Contributions to lossless coding of medical images using minimum rate predictors

Medical imaging compression is experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like MRI or CT. Furthermore, legal and diagnosis restrictions impose the use of lossless compression and data archival for several years. These facts create a demand for more efficient compression tools, used for archiving and communication. In this work, we first evaluate the performance of traditional medical image compression algorithms against that of recent state of the art lossless image encoders. We then propose a method to improve the Minimum Rate Predictors lossless encoder, by exploiting inter picture redundancy in volumetric anatomical images. Results show that the proposed method is more efficient than state of the art encoders, such as HEVC, by about 28.8%, and achieves a gain of up to 57.8% in compression ratio when compared with traditional methods.

A novel trilateral filter technique for depth map processing in 3D video coding

Free viewpoint video communication is one of the most interesting schemes for an immersive 3D experience, but it requires multiple views of the same scene. In order to limit the number of transmitted views, depth representations of the scene are used for depth image-based rendering, to generate virtual views at the receiver. Nevertheless, the video-plus-depth approach presents an additional burden of data that may be attenuated by coding down-sampled depth maps. This is able to achieve significant bit rate savings, specially for higher compression ratios, at the expense of some degradation on the quality of the decoded depth maps. However, as these techniques heavily rely on the quality of the depth maps, post-processing methods, such as filtering, are used to restore its quality. In this work we propose a new depth map processing method that is able to improve the image rendering quality. Experimental results show that a better rendering quality is achieved, when compared to other commonly used filters.

Is the chlorophyll derivative Zn(II)e 6 Me a good photosensitizer to be used in root canal disinfection

BACKGROUND The aim of this study was to assess antimicrobial efficacy and cytotoxic outcomes of a chlorophyll based photosensitizer (PS) Zn(II)chlorin e6 methyl ester (Zn(II)e6Me), when applied to human dentin discs and root blocks infected with 48 h biofilms. The results were compared with the ones obtained with FotoSan® (commercial Toluidine Blue O formulation) and 3% sodium hypochlorite (NaOCl). METHODOLOGY Dentin and root blocks were infected with mixed biofilms of Enterococcus faecalis and Candida albicans; exposed for 15 min to 0.1 mg/mL of Zn(II)e6Me or Fotosan® and then irradiated with red light (627 nm, 75 mW, 3150 J/cm2) for 90 s or treated with NaOCl. Biofilm removal was calculated with safranin red assay and biofilm cells viability with XTT® assay. The PSs cytotoxicity was evaluated over human apical papilla primary cell line (hAPCs) with AlamarBlue® assay and cell morphology assessed with widefield fluorescence microscopy. RESULTS At dentin discs, the chlorophyll derivative performed better in biofilm removal (59.1%) than FotoSan® agent (57.5%), however, with lower efficacy than NaOCl (68.1%) (P = 0.0185). Conversely, at the root block, the chlorophyll Zn(II)e6Me (79.7%) present better antimicrobial efficacy than NaOCl (75.5%) and the disinfection pattern was more consistent at inner and outer samples for the former. No dark or photoinduced cytotoxic outcomes were detected for Zn(II)e6Me over human cells at 24 and 48 h when compared with other PSs (FotoSan®, Rose Bengal and 5,10,15,20-tetrakis(1-methylpyridinium-4-yl)porphyrin - TMPyP). CONCLUSIONS The chlorophyll derivative Zn(II)e6Me showed adequate antimicrobial efficacy, performing better in mixed biofilm removal than FotoSan® in both experimental conditions. No cytotoxic effects over human apical papilla cells were identified for this chlorophyll derivative, subsequently it deserves further evaluation to assess its potential use in endodontic therapy.

Exploring the Effects of Data Distribution in Missing Data Imputation

In data imputation problems, researchers typically use several techniques, individually or in combination, in order to find the one that presents the best performance over all the features comprised in the dataset. This strategy, however, neglects the nature of data (data distribution) and makes impractical the generalisation of the findings, since for new datasets, a huge number of new, time consuming experiments need to be performed. To overcome this issue, this work aims to understand the relationship between data distribution and the performance of standard imputation techniques, providing a heuristic on the choice of proper imputation methods and avoiding the needs to test a large set of methods. To this end, several datasets were selected considering different sample sizes, number of features, distributions and contexts and missing values were inserted at different percentages and scenarios. Then, different imputation methods were evaluated in terms of predictive and distributional accuracy. Our findings show that there is a relationship between features’ distribution and algorithms’ performance, and that their performance seems to be affected by the combination of missing rate and scenario at state and also other less obvious factors such as sample size, goodness-of-fit of features and the ratio between the number of features and the different distributions comprised in the dataset.

Lossless coding of light field images based on minimum-rate predictors

Abstract Recent developments in light field acquisition and computational photography are driving new research efforts on light field encoding methods, capable of exploiting the specific features of this type of visual data. This paper presents a research study of lossless light field image compression, using Minimum-Rate Predictors (MRP) and mainstream image and video encoders. The research is focused on three light field representation formats: lenslet images, stack of sub-aperture images and epipolar images. The main contributions of this work are the ‘Spiral-blackend’ serialization method and the use of MRP for the lossless compression of light fields with joint encoding of RGB data. The results show that the lenslet format yields lower compression efficiencies than other formats. Furthermore, it is demonstrated that the MRP algorithm consistently outperforms HEVC-RExt, JPEG2000, JPEG-LS and CALIC when light fields are represented by either a stack of sub-aperture or epipolar images.

A method to improve HEVC lossless coding of volumetric medical images

Abstract Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which enable more accurate processing, feature analysis and medical diagnosis. However, the increase in resolution also requires a growing amount of data to be stored, processed and exchanged or transmitted through networks. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this paper we propose a method to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, and medical sequences such as X-ray Angiography images, using the latest standard High Efficiency Video Encoder (HEVC). New pixel-wise prediction techniques are proposed to extend the current HEVC lossless tools, based on Least-Squares Prediction (LSP). Experimental results show a bitrate reduction of over 44%, when compared to DICOM recommended encoders, and 13.8% when compared to standard lossless HEVC, for 8 bpp volumetric images, and over 8% and 4.6%, respectively, for volumetric images using more than 8 bpp.

Compression of medical images using MRP with bi-directional prediction and histogram packing

Medical imaging technology has become essential for the improvement of medical practice. This led to advances in the technology, namely in image sampling resolutions, pixel bit-depth and inter slice resolution. Additionally, common use of medical images, the life expectancy of patients and legal restrictions led to increasing storage costs. Therefore, efficient compression of medical image data is in high demand, for archiving and transmission. In this work we propose to improve the compression efficiency of the Minimum Rate Predictors lossless encoder, by adding bi-directional prediction support and a histogram packing technique. The results show that the proposed method presents a higher compression efficiency than state-of-the-art HEVC encoder. The compression efficiency is improved by 20%, on average, when compared to HEVC and by 46.1% when compared with the original MRP algorithm.