Designing a Cockpit for Image Quality Evaluation

Image Quality (IQ) as assessed by humans is a concept hard to be defined, since it relies on many different features, including both low level and high level visual characteristics. Image luminance, contrast, color distribution, smoothness, presence of noise or of geometric distortions are some examples of low level cues usually contributing to image quality. Aesthetic canons and trends, displacement of the objects in the scene, significance and message of the imaged visual content are instances of the high level (i.e. semantic) concepts that may be involved in image quality assessment. Despite subjective evaluation of IQ being very popular in many applications (e.g. image restoration, colorization and noise removal), it may be scarcely reliable due to subjectivity issues and biases. Therefore, an objective evaluation, i.e. an image quality assessment based on visual features extracted from the image and mathematically modelled, is highly desirable, since it guarantees the repeatability of the results and it enables the automation of image quality measurements. Here the crucial point lies in the detection of visual elements salient for IQ. Many objective, numerical measures have been proposed in the literature. They differ from one another in the features considered to be relevant to IQ, and in the presence of a reference image, an image of \u201cperfect\u201d quality with which to compare the image to be evaluated. Objective measures are thus broadly classified as full-reference, reduced-reference or no-reference, according to the availability of reference information. Due to the complexity of the IQ assessment process, a single measure may be not robust and accurate enough to capture and numerically summarize all the aspects concurring to IQ. Therefore, we propose to employ multiple objective IQ measures assembled in a cockpit of objective IQ measures. This cockpit should be designed to offer not only an extensive analysis and overview of features relevant to IQ, but also as a tool to automate the selection of machine vision algorithms devoted to image enhancement. In this work we describe a preliminary version of a cockpit, and we employ it to assess a set of images of the same scene acquired under different conditions, with different devices or even processed by computer algorithms

Automatic quantification of histochemical images of cancerous tissue samples: a method based on a computational model of human color vision

Protein Ki 67 is present in replicating nuclei It is therefore used as a marker of tumor aggressiveness Its quantification is important for diagnostic and prognostic evaluations For pKi 67 quantification, the Ki 67 index is estimated by clinicians Ki 67 index the percentage of marked tumor nuclei with respect to all tumour nuclei BUT histochemical images have high dimension and high resolution Human counting procedures are labourious, time consuming, error prone, affected by high inter and intra variability. Clinicians need automatic counting procedures to aid their work. sections (marked for pKi 67 of cancerous tissue They show high color/luminance variability, problems due to the biological procedures applied for tissue staining (tissue cuts, tissue folds, unwanted and unspecific colorations) and image acquisition acquisition ( noise). The aim: develop an automatic system estimating the Ki67 index: the percentage of replicating cells (brownish) with respect to all cells (brownish+bluish). Problem solved with stress + simple thresholding+ supervised learner. Expert users manually select three training sample sets: 1) marked nuclei; 2) not marked nuclei; 3) background tissue. The color of each training pixel p is coded as Color(p)=[R(p),B(p),H(p)] and a bayesian tree is trained (R,B from RGB color space, H from HSV c olo r space). Training sets allow computing the median area of marked nuclei (medAOn), and the median area of not marked nuclei (medAOff). Two index estimations (IE1 and IE2) Correlation(IE1,E30) > Correlation(IE1,E15) Correlation(IE2,E30) > Correlation(IE2,E15) E15 = estimates of expert with 15 years of experience E30 = estimates of expert with 15 years of experience (bayesian)

Prediction of benzimidazole therapy duration with PET/CT in inoperable patients with alveolar echinococcosis

Alveolar echinococcosis is a rare parasitic disease, most frequently affecting the liver, as a slow-growing tumor-like lesion. If inoperable, long-term benzimidazole therapy is required, which is associated with high healthcare costs and occasionally with increased morbidity. The aim of our study was to determine the role 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in staging of patients with alveolar echinococcosis and to identify quantitative imaging parameters related to patient outcome and/or duration of benzimidazole therapy. In this single-center retrospective cohort study, 47 PET/CT performed for staging in patients with confirmed alveolar echinococcosis were analysed. In 43 patients (91%) benzimidazole therapy was initiated and was successfully stopped after a median of 870 days (766-2517) in 14/43 patients (33%). In inoperable patients, tests for trend of survivor functions displayed clear trends for longer benzimidazole therapy duration (p = 0.05; n = 25), and for longer time intervals to reach non-detectable serum concentration of Em-18 antibodies (p = 0.01, n = 15) across tertiles of SUVratio (maximum standardized uptake value in the echinococcus manifestation compared to normal liver tissue). Hence, in inoperable patients with alveolar echinococcosis, PET/CT performed for staging may predict the duration of benzimidazole therapy

Predictive value of baseline [18f]fdg pet/ct for response to systemic therapy in patients with advanced melanoma

Background/Aim: To evaluate the association between baseline [18F]FDG-PET/CT tumor burden parameters and disease progression rate after first-line target therapy or immunotherapy in advanced melanoma patients. Materials and Methods: Forty four melanoma patients, who underwent [18F]FDG-PET/CT before first-line target therapy (28/44) or immunotherapy (16/44), were retrospectively analyzed. Whole-body and per-district metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated. Therapy response was assessed according to RECIST 1.1 on CT scan at 3 (early) and 12 (late) months. PET parameters were compared using the Mann–Whitney test. Optimal cut-offs for predicting progression were defined using the ROC curve. PFS and OS were studied using Kaplan–Meier analysis. Results: Median (IQR) MTVwb and TLGwb were 13.1 mL and 72.4, respectively. Non-responder patients were 38/44, 26/28 and 12/16 at early evaluation, and 33/44, 21/28 and 12/16 at late evaluation in the whole-cohort, target, and immunotherapy subgroup, respectively. At late evaluation, MTVbone and TLGbone were higher in non-responders compared to responder patients (all p < 0.037) in the whole-cohort and target subgroup and MTVwb and TLGwb (all p < 0.022) in target subgroup. No significant differences were found for the immunotherapy subgroup. No metabolic parameters were able to predict PFS. Controversially, MTVlfn, TLGlfn, MTVsoft + lfn, TLGsoft + lfn, MTVwb and TLGwb were significantly associated (all p < 0.05) with OS in both the whole-cohort and target therapy subgroup. Conclusions: Higher values of whole-body and bone metabolic parameters were correlated with poorer outcome, while higher values of whole-body, lymph node and soft tissue metabolic parameters were correlated with OS

Work memories in Super 8: Searching a frame quality metric for movie restoration assessment

This paper describes the digital acquisition and restoration of a Super 8 documentary on the origins of citizens\u2019 awareness on paper recycling in the city of Brescia, Lombardy, northern Italy. This film is currently stored in Fondazione ASM archives, and its acquisition is part of a larger project of preservation of industrial memories in Italy. Here we describe the tools used for the acquisition and the post-processing with a Spatial Colour Algorithm (SCA) for unsupervised colour and contrast enhancement. This method provides a semi-automatic image restoration, whose quality has been tested through different evaluations. In this paper, some advantages of the use of SCAs in the restoration pipeline are presented, together with a preliminary discussion on the problem of assessing frame restoration results. To this aim, we present objective vs. subjective evaluation, and some issues associated with this complex task. The most used full-reference and no-reference metrics have been used, together with alternative low-level descriptors. Data and examples are presented and discussed

About Glare And Luminance Measurements

Glare is un unwanted scattering of light occurring upon its propagation through optical media, whose scarcely predictable, scene-dependent effects are potentially disrupting in terms of accurate scene acquisition. This work starts from the idea of assessing the magnitude of glare in low dynamic range monitor layers during visualization. According to common practice, monitor dynamic ranges are computed as ratios of maximum to minimum luminance values separately acquired on full-screen black and white images. Avoiding the coexistence in the same image of maximum and minimum luminance, this method does not consider the effect of possible intra-layer glare. To measure possible intra-layer glare in a monitor, we have displayed images made up with black and white patterns of different sizes. Measuring these different patterns, we detected changes in the luminance of the black regions. At first we explained data as a glare effect. Measuring more carefully each regions through a masking cardboard with a hole, these differences were no more there. It was just glare, not from the monitor layers, but from the lens of the measuring instrument. To further investigate the issue, another setup was arranged whereby two color checkers were stationed behind a dimmable light source aiming away from them both, and directly into the luminance meter. We found that despite light being unable to fall directly on the color checkers, an increase of radiant power was paralleled by an upward drift in luminance values for all examined spots, more so for those lying the closest to a prominent lens flare within the device viewing field. These combined findings show us that no matter the accuracy of the measuring device, luminance information can neither be measured nor displayed correctly in the presence of glare in the instrument