A cockpit of multiple measures for assessing film restoration quality

In machine vision, the idea of expressing the quality of a films by a single value is very popular. Usually this value is computed by processing a set of image features with the aim of resembling as much as pos- sible a kind of human judgment of the film quality. Since human quality assessment is a complex mech- anism involving many different perceptual aspects, we believe that such approach may scarcely provide a comprehensive analysis. Especially in the field of digital movie restoration, a single score can hardly provide reliable information about the effects of the various restoring operations. For this reason we in- troduce an alternative approach, where a set of measures, describing over time basic global and local visual properties of the film frames, is computed in an unsupervised way and delivered to expert evalu- ators for checking the restoration pipeline and results. The proposed framework can be viewed as a car or airplane cockpit , whose parameters (i.e. the computed measures) are necessary to control the machine status and performance. This cockpit, which is publicly available online, would like to support the digital restoration process and its assessment

Ki67 nuclei detection and ki67-index estimation: A novel automatic approach based on human vision modeling

Background: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open. Results: In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions. Conclusions: The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes

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)

Defining and simulating open-ended novelty: requirements, guidelines, and challenges

The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community

Design for Mobile Mental Health:An Exploratory Review

A large number of mobile mental health apps are available to the public but current knowledge about requirements of designing such solutions is scarce, especially from sociotechnical and user centred points of view. Due to the significant role of mobile apps in the mental health service models, identifying the design requirements of mobile mental health solutions is crucial. Some of those requirements have been addressed individually in the literature, but there are few research studies that show a comprehensive picture of this domain. This exploratory review aims to facilitate such holistic understanding. The main search keywords of the review were identified in a cross-disciplinary requirements workshop. The search was started by finding some core references in the healthcare databases. A wider range of references then has been explored using a snowball method. Findings showed that there is a good understanding of individual design requirements in current literature but there are few examples of implementing a combination of different design requirements in real world products. The design processes specifically developed for mobile mental health apps are also rare. Most studies on operational mobile mental health apps address major mental health issues while prevention and wellbeing areas are underdeveloped. In conclusion, the main recommendations for designing future mobile mental health solutions include: moving towards sociotechnical and open design strategies, understanding and creating shared value, recognizing all dimensions of efficacy, bridging design and medical research and development, and considering an ecosystem perspective

End-User Development in Industry 4.0: Challenges and Opportunities

This position paper aims to discuss challenges and opportunities related to human-computer interaction technologies for Industry 4.0 and to explore the role that end-user development can play in new industrial scenarios. The paper highlights the gap between what Industry 4.0 and related enabling technologies promise and how the Operator 4.0 will be called on to change his/her work practice. End-user development and meta-design are here proposed as suitable methods to fill this gap and improve operators’ quality of work