Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images

Morphological granulometry for classification of evolving and ordered texture images.

In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images

Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

Supporting regional growth from the higher education community: the Energy Coast Campus Programme in West Cumbria

West Cumbria is a predominantly rural, but post-industrial region undergoing a transition from one that has been dominated by heavy industry over a 200 year period. The regional economy has latterly been dominated by one of the world’s largest nuclear technology hubs, which continues to influence the structure of the economy. The region has aspirations to evolve a high technology manufacturing base, with a continued strong role for nuclear, but with a more diversified economy, including an expanded focus on low carbon and renewable energy generation. The region has aspirations to evolve a high technology manufacturing base, with a continued strong role for nuclear, but with a more diversified economy. As part of this strategy, a large investment has been made to build a higher education community in this largely rural area, to support its strategic objectives to promote innovation through applied research, research demonstration, enterprise, business support, skills and training and other transformational actions. Three case studies are described in detail: the Cumbrian Centre for Health Technologies (CaCHeT), the Sustainable Energy Technology Group and the Knowledge Action Network (KAN). The lessons learned are evaluated and presented, with details of future plans

A case study of stakeholder perceptions of patient held records: the Patients Know Best (PKB) solution

Introduction: Patients Know Best (PKB) provide a patient portal with integrated, patient controlled digital care record. Patient controlled personal health records facilitate coordinated management of chronic disease through improved communications among, and about patients across professional and organizational boundaries. An NHS foundation trust hospital has used PKB to support self-management in patients with Inflammatory Bowel disease; this paper presents a case study of usage. Methods: The Stakeholder Empowered Adoption Model provided a framework for consulting variously placed stakeholders. Qualitative interviews with clinical stakeholders and a patient survey. Results: Clinicians reported PKB to have enabled a new way of managing stable patients, this facilitated clinical and cost effective use of specialist nurses; improved two-way communications, and more optimal use of outpatient appointments and consultant time. The portal also facilitated a single, rationalised pathway for stable patients, enabling access to information and pro-active support. For patients, the system was a source of support when unwell and facilitated improved communication with specialists. Three main barriers to adoption were identified, these related to concerns over security; risk averse attitudes of users; and problems with data integration. Conclusions: Patient controlled personal health records offer significant potential in supporting self-management. Digital connection to healthcare can help patients to understand their condition better and access appropriate, timely clinical advice

Using Telemedicine in Practice: Implications for Workforce Development

The aim of this article is to present a discussion of the impact of telemedicine on professional practice, and the implications for the workforce. Telemedicine, or the use of video-conferencing for remote consultations between clinician(s) and patients, is now a mature technology. Many pilot studies have taken place, generally showing positive benefits to patients. There is emerging evidence that the impact on staff is more mixed; with concerns about changes to job role, skills development, and poor understanding of the organisational benefits. Evidence also highlights enablers of successful telemedicine implementation, including senior leadership, peer motivation, understanding of patient benefits, and time for safe experimentation. Following a review of qualitative data from four case study telemedicine projects undertaken within the authors’ research group, evidence from published literature is discussed. The four projects explore telemedicine services provided between an acute hospital service and nursing homes (remote assessment of swallowing difficulties), an acute hospital service and home (video-link to renal patients undergoing home dialysis), between a specialist teaching hospital service and a district general hospital (fetal abnormalities ultrasound telemedicine clinic), and a survey of mental health professionals across acute and community services within a locality. The introduction of telemedicine at scale requires an organisational and system-level approach that recognises the specific challenges and issues for the workforce. Education and training need to be provided at all levels. In conclusion: there are significant opportunities to realise the benefits of remote consultations, to improve the patient experience and staff productivity, if workforce issues are addressed

Rural innovation ecosystems and leading wellbeing

Innovation ecosystems are an emerging concept to describe place-based clusters of companies and other organisations, interacting for growth, development and sustainability, often focused around an ‘anchor institution’. Most successful examples operate in urban contexts. Literature on rural innovation suggests that the nature and needs of rural businesses can be different. This article reviews some of the key themes, including skill needs, aspirations and motivations of rural professionals, suitability of anchor institutions and leadership. Rural areas are known to have different demographic structures from urban ones. In particular, the tendency to attract highly qualified, but growth-reluctant, professionals, as ‘in-migrants’ is discussed. We hypothesise that a successful rural innovation ecosystem should focus more on sustainability, wellbeing and balance, rather than primarily on ambition and growth. The needs of individuals may also be more important than those of business units and a focus on skills development could be desirable

Renal telemedicine through video-as-a-service delivered to patients on home dialysis: a qualitative study on the renal care team members’ experience

Background: The Lancashire Teaching Hospitals NHS Trust in the UK has been providing renal care through video-as-a-service (VAAS) to patients since 2013, with support from the North West NHS Shared Infrastructure Service, a collaborative team that supports information and communication technology use in the UK National Health Service. Introduction: Renal telemedicine offered remotely to patients on home dialysis supports renal care through the provision of a live high-quality video link directly to unsupported patients undergoing haemodialysis at home. Home haemodialysis is known to provide benefits to patients, particularly in making them more independent. The use of a telemedicine video-link in Lancashire and South Cumbria, UK, further reduces patient dependence on the professional team. Objective: The purpose of this paper is to present the perspectives of the renal care team members using the renal telemedicine service to understand the perceived benefits and issues with the service. Method: Ten semi-structured interviews with members of the renal care team (two renal specialists, one matron, two renal nurses, one business manager, one renal technical services manager, two IT technicians and one hardware maintenance technician) were conducted. Thematic analysis was undertaken to analyse the qualitative data. Results: A range of incremental benefits to the renal team members were reported, including more efficient use of staff time, reduced travel, peace of mind and a strong sense of job satisfaction. Healthcare staff believed that remote renal care through video was useful, encouraged concordance and could nurture confidence in patients. Key technological issues and adjustments which would improve the renal telemedicine service were also identified. Conclusion: The impact of renal telemedicine was positive on the renal team members. The use of telemedicine has been demonstrated to make home dialysis delivery more efficient and safe. The learning from staff feedback could inform development of services elsewhere

Editorial: Leading wellbeing in rural contexts

“Leading Wellbeing in Rural Contexts” is the theme of this Special Issue of The Journal of Corporate Citizenship (JCC). From the outset, the Special Issue was envisaged by the editors very much as an opening foray into an area of academic inquiry with a relatively unexplored body of literature to date and without a formal disciplinary “home”. It is very much the start of the discussion on this topic and one that we look forward to developing further in the future