• Inter-Ethnic and Demic-Group Variations in Craniofacial Anthropometry: A Review

      Jilani, Shelina K.; Ugail, Hassan; Logan, Andrew J. (2019)
      Craniofacial anthropometry plays an important role in facial structure. This review paper evaluates existing research surrounding population norms of studied facial parameters. The purpose is two-fold: (1) to determine variations in facial measurements due to demi-group or ethnic variations based on traditional (direct) caliper based and image based (indirect) anthropometric methods. (2) to compare where possible, measured facial parameters between referenced studies. Inter and intra-population variations in addition to sexual dimorphism of facial parameters such as the nose and eyes, singularly or in combination with one another, have been concluded. Ocular measurements have exhibited ethnic variations between males and females of the Saudi, Turkish, Egyptian and Iranian group. Moreover, demic variations are reported when the native language has been used a key criterion. It has been concluded that with the current state of migration and inter-demic marriages, the study of homogenous populations will prove difficult. Subsequently, this will result in ambiguous physical traits that are not representative for any one demic or ethnic population. In this paper, results for the following adult male and female populations have been discussed: African American, Azerbaijani, Caribbean, Chinese, Croatian, Egyptian, Italian, Iranian, Turkish, Saudi Arabian, Syrian and South African. The qualitative research presented serves as a knowledge base for learners and strikes up thought provoking concepts about the direction anthropometrical research is heading.
    • A machine learning approach for ethnic classification: the British Pakistani face

      Khalid Jilani, Shelina; Ugail, Hassan; Bukar, Ali M.; Logan, Andrew J.; Munshi, Tasnim (2017)
      Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed framework is based on the extraction of geometric features using 10 anthropometric facial landmarks, within a purpose-built, novel database of 135 multi-ethnic and multi-racial subjects and a total of 675 face images. Image dimensionality was reduced using Principle Component Analysis and Partial Least Square Regression. Classification was performed using Linear Support Vector Machine. The results of this framework are promising with 71.11% ethnic classification accuracy using a PCA algorithm + SVM as a classifier, and 76.03% using PLS algorithm + SVM as a classifier.
    • A Non-invasive 2D Digital Imaging Method for Detection of Surface Lesions Using Machine Learning

      Hussain, Nosheen; Cooper, Patricia A.; Shnyder, Steven D.; Ugail, Hassan; Bukar, Ali M.; Connah, David (2017)
      As part of the cancer drug development process, evaluation in experimental subcutaneous tumour transplantation models is a key process. This involves implanting tumour material underneath the mouse skin and measuring tumour growth using calipers. This methodology has been proven to have poor reproducibility and accuracy due to observer variation. Furthermore the physical pressure placed on the tumour using calipers is not only distressing for the mouse but could also lead to tumour damage. Non-invasive digital imaging of the tumour would reduce handling stresses and allow volume determination without any potential tumour damage. This is challenging as the tumours sit under the skin and have the same colour pattern as the mouse body making them hard to differentiate in a 2D image. We used the pre-trained convolutional neural network VGG-16 and extracted multiple layers in an attempt to accurately locate the tumour. When using the layer FC7 after RELU activation for extraction, a recognition rate of 89.85% was achieved.
    • The use of thermographic imaging to evaluate therapeutic response in human tumour xenograft models

      Hussain, Nosheen; Connah, David; Ugail, Hassan; Cooper, Patricia A.; Falconer, Robert A.; Patterson, Laurence H.; Shnyder, Steven D. (2016-08-05)
      Non-invasive methods to monitor tumour growth are an important goal in cancer drug development. Thermographic imaging systems offer potential in this area, since a change in temperature is known to be induced due to changes within the tumour microenvironment. This study demonstrates that this imaging modality can be applied to a broad range of tumour xenografts and also, for the first time, the methodology’s suitability to assess anti-cancer agent efficacy. Mice bearing subcutaneously implanted H460 lung cancer xenografts were treated with a novel vascular disrupting agent, ICT-2552, and the cytotoxin doxorubicin. The effects on tumour temperature were assessed using thermographic imaging over the first 6 hours post-administration and subsequently a further 7 days. For ICT-2552 a significant initial temperature drop was observed, whilst for both agents a significant temperature drop was seen compared to controls over the longer time period. Thus thermographic imaging can detect functional differences (manifesting as temperature reductions) in the tumour response to these anti-cancer agents compared to controls. Importantly, these effects can be detected in the first few hours following treatment and therefore the tumour is observable non-invasively. As discussed, this technique will have considerable 3Rs benefits in terms of reduction and refinement of animal use.
    • Using avatars in weight management settings: a systematic review

      Horne, M.; Hill, A.; Murells, T.; Ugail, Hassan; Irving; Chinnadorai, R.; Hardy, Maryann L. (2020-03)
      Background: Obesity interventions rely predominantly on managing dietary intake and/or increasing physical activity but sustained adherence to behavioural regimens is often poor. Avatar technology is well established within the computer gaming industry and evidence suggests that virtual representations of self may impact real-world behaviour, acting as a catalyst for sustained weight loss behaviour modification. However, the effectiveness of avatar technology in promoting weight loss is unclear. Aims: We aimed to assess the quantity and quality of empirical support for the use of avatar technologies in adult weight loss interventions. Method: A systematic review of empirical studies was undertaken. The key objectives were to determine if: (i) the inclusion of avatar technology leads to greater weight loss achievement compared to routine intervention; and (ii) whether weight loss achievement is improved by avatar personalisation (avatar visually reflects self). Results: We identified 6 papers that reported weight loss data. Avatar-based interventions for weight loss management were found to be effective in the short (4–6 weeks) and medium (3–6 months) term and improved weight loss maintenance in the long term (12 months). Only 2 papers included avatar personalisation, but results suggested there may be some added motivational benefit. Conclusions: The current evidence supports that avatars may positively impact weight loss achievement and improve motivation. However, with only 6 papers identified the evidence base is limited and therefore findings need to be interpreted with caution.