Dermoscopic dark corner artifacts removal: Friend or foe?

Background and Objectives: One of the more significant obstacles in classification of skin cancer is the presence of artifacts. This paper investigates the effect of dark corner artifacts, which result from the use of dermoscopes, on the performance of a deep learning binary classification task. Previous research attempted to remove and inpaint dark corner artifacts, with the intention of creating an ideal condition for models. However, such research has been shown to be inconclusive due to a lack of available datasets with corresponding labels for dark corner artifact cases. Methods: To address these issues, we label 10,250 skin lesion images from publicly available datasets and introduce a balanced dataset with an equal number of melanoma and non-melanoma cases. The training set comprises 6126 images without artifacts, and the testing set comprises 4124 images with dark corner artifacts. We conduct three experiments to provide new understanding on the effects of dark corner artifacts, including inpainted and synthetically generated examples, on a deep learning method. Results: Our results suggest that introducing synthetic dark corner artifacts which have been superimposed onto the training set improved model performance, particularly in terms of the true negative rate. This indicates that deep learning learnt to ignore dark corner artifacts, rather than treating it as melanoma, when dark corner artifacts were introduced into the training set. Further, we propose a new approach to quantifying heatmaps indicating network focus using a root mean square measure of the brightness intensity in the different regions of the heatmaps. Conclusions: The proposed artifact methods can be used in future experiments to help alleviate possible impacts on model performance. Additionally, the newly proposed heatmap quantification analysis will help to better understand the relationships between heatmap results and other model performance metrics

Synthesising facial macro-and micro-expressions using reference guided style transfer

Long video datasets of facial macro-and micro-expressions remains in strong demand with the current dominance of data-hungry deep learning methods. There are limited methods of generating long videos which contain micro-expressions. Moreover, there is a lack of performance metrics to quantify the generated data. To address the research gaps, we introduce a new approach to generate synthetic long videos and recommend assessment methods to inspect dataset quality. For synthetic long video generation, we use the state-of-the-art generative adversarial network style transfer method—StarGANv2. Using StarGANv2 pre-trained on the CelebA dataset, we transfer the style of a reference image from SAMM long videos (a facial micro-and macro-expression long video dataset) onto a source image of the FFHQ dataset to generate a synthetic dataset (SAMM-SYNTH). We evaluate SAMM-SYNTH by conducting an analysis based on the facial action units detected by OpenFace. For quantitative measurement, our findings show high correlation on two Action Units (AUs), i.e., AU12 and AU6, of the original and synthetic data with a Pearson’s correlation of 0.74 and 0.72, respectively. This is further supported by evaluation method proposed by OpenFace on those AUs, which also have high scores of 0.85 and 0.59. Additionally, optical flow is used to visually compare the original facial movements and the transferred facial movements. With this article, we publish our dataset to enable future research and to increase the data pool of micro-expressions research, especially in the spotting task

Automated Analysis and Quantification of Human Mobility using a Depth Sensor

Analysis and quantification of human motion to support clinicians in the decision-making process is the desired outcome for many clinical-based approaches. However, generating statistical models that are free from human interpretation and yet representative is a difficult task. In this work, we propose a framework that automatically recognises and evaluates human mobility impairments using the Microsoft Kinect One depth sensor. The framework is composed of two parts. Firstly, it recognises motions, such as sit-to-stand or walking 4 metres, using abstract feature representation techniques and machine learning. Secondly, evaluation of the motion sequence in the temporal domain by comparing the test participant with a statistical mobility model, generated from tracking movements of healthy people. To complement the framework, we propose an automatic method to enable a fairer, unbiased approach to label motion capture data. Finally, we demonstrate the ability of the framework to recognise and provide clinically relevant feedback to highlight mobility concerns, hence providing a route towards stratified rehabilitation pathways and clinician led interventions

Manual Whisker Annotator (MWA): A Modular Open-Source Tool

Rodents are key to generating translational data for healthcare research. Behavioural analyses, in particular, are integral to the non-invasive monitoring of rodent health and welfare. Finding quantitative behavioural measures mitigates stress, allowing for the animal behave freely while also enabling the same animal to be studied over the time-course of its life. Locomotion and whisking are both such quantitative behavioural measures, and have been found to be significantly impacted in rodent models of neurodegenerative disease. While automatic trackers of whiskers and locomotion exist, a manual tracker is required to validate these approaches, and also to annotate complex videos where these automatic versions fail. Manually annotating whiskers for research purposes is a long and tedious task and current software does little to provide an intuitive and simple interface to carry out this task. This led to the creation of the Manual Whisker Annotator (MWA). MWA is an open source, portable whisker annotation tool developed for the Windows platform. Not only does MWA make the process much quicker, it also provides added statistical tools to analyse the data. MWA was developed in C# and WPF using the .NET framework, and could be used in any situation where annotating or tracking multiple targets is desired

An investigation on local wrinkle-based extractor of age estimation

Research related to age estimation using face images has become increasingly important due to its potential use in various applications such as age group estimation in advertising and age estimation in access control. In contrast to other facial variations, age variation has several unique characteristics which make it a challenging task. As we age, the most pronounced facial changes are the appearance of wrinkles (skin creases), which is the focus of ageing research in cosmetic and nutrition studies. This paper investigates an algorithm for wrinkle detection and the use of wrinkle data as an age predictor. A novel method in detecting and classifying facial age groups based on a local wrinkle-based extractor (LOWEX) is introduced. First, each face image is divided into several convex regions representing wrinkle distribution areas. Secondly, these areas are analysed using a Canny filter and then concatenated into an enhanced feature vector. Finally, the face is classified into an age group using a supervised learning algorithm. The experimental results show that the accuracy of the proposed method is 80% when using FG-NET dataset. This investigation shows that local wrinkle-based features have great potential in age estimation. We conclude that wrinkles can produce a prominent ageing descriptor and identify some future research challenges. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved

Validation of a New Semi-Automated Technique to Evaluate Muscle Capillarization.

The method of capillary domains has often been used to study capillarization of skeletal and heart muscle. However, the conventional data processing method using a digitizing tablet is an arduous and time-consuming task. Here we compare a new semi-automated capillary domain data collection and analysis in muscle tissue with the standard capillary domain method. The capillary density (1481 ± 59 vs. 1447 ± 54 caps mm(-2); R(2):0.99; P < 0.01) and heterogeneity of capillary spacing (0.085 ± 0.002 vs. 0.085 ± 0.002; R(2):0.95; P < 0.01) were similar in both methods. The fiber cross-sectional area correlated well between the methods (R(2):0.84; P < 0.01) and did not differ significantly (~8 % larger in the old than new method at P = 0.08). The latter was likely due to differences in outlining the contours between the two methods. In conclusion, the semi-automated method gives quantitatively and qualitatively similar data as the conventional method and saves a considerable amount of time

Wrinkle Detection Using Hessian Line Tracking

Wrinkles play an important role in face-based analysis. They have been widely used in applications such as facial retouching, facial expression recognition and face age estimation. Although a few techniques for wrinkle analysis have been explored in the literature, poor detection limits the accuracy and reliability of wrinkle segmentation. Therefore, an automated wrinkle detection method is crucial to maintain consistency and reduce human error. In this paper, we propose Hessian Line Tracking (HLT) to overcome the detection problem. HLT is composed of Hessian seeding and directional line tracking. It is an extension of a Hessian filter; however it significantly increases the accuracy of wrinkle localization when compared with existing methods. In the experimental phase, three coders were instructed to annotate wrinkles manually. To assess the manual annotation, both intra- and inter-reliability were measured, with an accuracy of 94% or above. Experimental results show that the proposed method is capable of tracking hidden pixels; thus it increases connectivity of detection between wrinkles, allowing some fine wrinkles to be detected. In comparison to the state-of-the-art methods such as the CUla Method (CUM), FRangi Filter (FRF), and Hybrid Hessian Filter (HHF), the proposed HLT yields better results, with an accuracy of 84%. This work demonstrates that HLT is a remarkably strong detector of forehead wrinkles in 2D images

Using nexus thinking to identify opportunities for mangrove management in the Klang Islands, Malaysia

Despite wide recognition of the multiple ecosystem services provided by mangroves, they continue to experience decline and degradation especially in the face of urbanization. Given the interplay between multiple resources and stakeholders in the fate of mangroves, mangrove management can be framed as a nexus challenge and nexus thinking used to identify potential solutions. Using the Klang Islands, Malaysia, as a case study site, this paper characterizes the mangrove nexus and stakeholders visions for the future to identify potential options for future management. Through a series of stakeholder workshops and focus group discussions conducted over two years results show that local communities can identify benefits from mangroves beyond the provisioning of goods and significant impacts to their lives from mangrove loss. While better protected and managed mangroves remained a central part of participants' visions for the islands, participants foresaw a limited future for fishing around the islands, preferring instead alternative livelihood opportunities such as eco-tourism. The network of influencers of the Klang Islands’ mangroves extends far beyond the local communities and many of these actors were part of the visions put forward. Stakeholders with a high interest in the mangroves typically have a low influence over their management and many high influence stakeholders (e.g. private sector actors) were missing from the engagement. Future nexus action should focus on integrating stakeholders and include deliberate and concerted engagement with high influence stakeholders while at the same time ensuring a platform for high interest/low influence groups. Fortifying existing plans to include mangroves more explicitly will also be essential. Lessons learnt from this study are highly relevant for coastal mangrove systems elsewhere in the Southeast Asian region

SAMM: A Spontaneous Micro-Facial Movement Dataset

Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis

Deep learning for mass detection in Full Field Digital Mammograms

© 2020 The Authors In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of ∼80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening