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In the UK there has been a shift over the last decade towards the setting of targets by commissioning or regulatory bodies so that health services provide improved levels of care. For mental health services this has occurred in three phases: phase one related to simple key performance indicators (for example collection of patients' diagnostic codes); phase two placed greater emphasis on more meaningful clinical outcome data; the third and most recent phase placed prominence on patient‐focused data collection. We report the development and preliminary findings of a piloting of a patient‐related outcome measure (PROM) and patient‐related experience measure (PREM) created specifically for older adult mental health services in Central and North West London NHS Foundation Trust. PROMs/PREMs are in keeping with an increased focus on patients' experience of health care, the provision of quality services and various key policies such as the recent government white paper Equity and Excellence: Liberating the NHS.

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates.

The vision system that has been developed consists of two major components covering the opto‐mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface.

The inspection system was developed on 400 slates to determine the threshold settings that give the best trade‐off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples.

The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory‐type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system.

The development of a real‐time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non‐uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set‐up and the development of multi‐component image‐processing inspection algorithm.

This paper details a free image analysis and software development environment for machine vision application development. The environment provides high‐level access to over 300 image manipulation, processing and analysis algorithms through a well‐defined and easy to use graphical interface. Users can extend the core library using the developer's interface via a plug‐in which features automatic source code generation, compilation with full error feedback and dynamic algorithm updates. Also discusses key issues associated with the environment and outline the advantages in adopting such a system for machine vision application development.

The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters.

The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.

The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture.

Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding.

This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

Despite older adult community mental health team (CMHT) workers inevitably encountering service users with alcohol use disorders (AUDs), they have a limited evidence base for working with this group. This paper aims to argue against a wholesale transfer of knowledge and practices from working age adults with dual diagnosis to the older age group.

The paper aims to address the scant attention given to the needs of older service users with a dual diagnosis by detailing the progress of four older adult service users with AUDs in a CMHT.

This case study series illuminates some of the clinical and professional dilemmas and challenges encountered when working with this group. Suggestions of how to respond to some of these challenges are provided by focussing on aspects of identification, assessment and treatment.

This paper provides a catalyst to further discussion, debate and publications on the issue of older people misusing alcohol by presenting and reflecting on the issues raised by four practice vignettes.