Retinal vessel segmentation has important application value in clinical diagnosis. If experts manually segment the retinal vessels, the workload is heavy, and the result is strong subjectively. However, some existing automatic segmentation methods have the problems of incomplete vessel segmentation and low-segmentation accuracy. In order to solve the above problems, this study proposes a retinal vessel segmentation method based on task-driven generative adversarial network (GAN). In the generative model, a U-Net network is used to segment the retinal vessels. In the discriminative model, multi-scale discriminators with different receptive fields are used to guide the generative model to generate more details. On the other hand, in view of the uncontrollable characteristics of the data generated by the traditional GAN, a task-driven model based on perceptual loss is added to traditional GAN for feature matching, which makes the generated image more task-specific. Experimental results show that the accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of the proposed method on data set digital retinal images for vessel extraction are 96.83, 80.66, 98.97 and 0.9830%, respectively.

Using the vibration signals, the facial tissue characteristics may be utilised for the detection of nasal diseases. In this study, the tissue characteristics were specified by applying constant frequency vibration signals to the facial tissue. The temperature changes caused by an external vibration source applied to the human face were investigated using thermal imaging techniques. Vibrations were applied to the forehead, right cheek, and left cheek regions of the facial tissue. Temperature differences were examined using dynamic and static analyses. Temperature increases of 500, 562, and 606 m°C were acquired in the F region, MR, and ML regions, respectively. While the F region has the lowest soft tissue thickness and temperature difference, the ML region has the highest values. The temperature difference between ML and F regions was acquired as 106 m°C. The temperature distributions of the facial area indicate that the change of the temperature is lower in the regions where the soft tissue thickness is low, and higher in the regions where the soft tissue thickness is high. Therefore, the thickness information about the soft tissue can be provided from the temperature distribution of the facial area after the application of the vibration signal.

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Rényi's two-dimensional entropy with a cultural algorithm (2DRCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques.

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time-consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X-ray images of normal, pneumonia and coronavirus disease-2019 (COVID-19) patients and process them to boost the COVID-specific features (opacities etc.), which enable to perform sensitive identification of COVID-19 patients. The sets of original and processed images are used with a stack of pre-trained deep models for ensemble learning. They used VGG-16 as base-learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base-learner predictions. The proposed fusion-based model is trained and tested for three types of classification, TYPE-I: binary (NORMAL/ABNORMAL), TYPE-II: binary (PNEUMONIA/COVID-19) and TYPE-III: multi-class (NORMAL/PNEUMONIA/COVID-19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID-19 patients and thereby mitigating the effect of the virus.

Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi-source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT–MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high-frequency NSST subbands get fused using max-rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT–MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better-fused images and outperforms other existing methods in both visual and quantitative assessments.

Vision is one of the most valued of human sensory perceptions, and vision loss is associated with a significant decrease in quality of life as well as serious medical, psychological, social and financial consequences. Due to the high value people place on vision, ophthalmologists often find that patients are motivated to take an active role in reducing their risk for vision loss. Age-related macular degeneration (AMID) is the leading cause of irreversible vision loss in the western world. Despite major advances in treating this condition over the past two decades, additional efforts are needed to significantly alter current rates of visual decline due to AMID. This unmet need provides an opportunity to utilise home monitoring technology to enable self-aware AMID patients to preserve their vision. Remote patient monitoring is growing in clinical applicability generally, and AMID is an excellent target for this valuable approach to patient care. The ForeseeHome®preferential hyperacuity perimeter is a telemedicine home-based monitoring system and has been proven to improve visual outcomes in patients suffering from AMID. The development of ForeseeHome is the result of a global cooperative effort to change the lives of people with AMID by using a simple at-home test. As is true for other successes in biomedicine, this program was founded on excellent basic science, strong engineering, an experienced, dedicated team and well-designed clin-ical trials showing unquestionable efficacy.

This work aims at developing an automated ensemble-based glioma grade classification framework that classifies glioma into low-grade glioma (LGG) and high-grade glioma (HGG). Discriminant features are extracted using the Gabor filter bank and concatenated in a vectorised form. The feature set is then divided into k subsets of features. An ensemble of base classifiers known as rotation forest is employed for classification purpose. Independent components analysis (ICA) is applied on every feature subset and independent features are extracted. Each classifier in the ensemble is trained with these independent features from all the subset of features. These k feature subsets are responsible for different rotations during the training phase. This results in classifier diversity in the ensemble. Extensive experiments are conducted on benchmark BraTS 2017 data set and comparative analysis reveals that the proposed framework outperforms the competitive techniques in terms of various performance metrics. Data-augmentation technique, synthetic minority over-sampling technique is applied to oversample minority class samples alleviate class biasness problem. The proposed classification framework achieves an accuracy of 98.63%, dice similarity coefficient of 0.98 and sensitivity of 0.96. The authors conduct different comparative experiments with state-of-the-art ensemble-based, deep learning-based and traditional machine learning-based classification approaches to validate the performance of the proposed framework.

Image processing applications remarkably contributes to modern ophthalmology. This technology is designed to analyse the characteristics of the human eye microvasculature images. The retinal microvasculature is an excellent non-invasive screening window for the assessment of systemic diseases such as diabetes, hypertension, and stroke. Retinal microvasculature character such as widening vessel diameter is recognised as an analysable feature for stroke or transient ischemic attack for predicting the progression of this pathology. Thus, in this study, a computer-assisted method has been developed for this task applying the Euclidean distance transform (EDT) technique. This newly developed algorithm computes the Euclidean distance of the remaining white pixels on the area of interest. Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of Retinal Vessel Image set for Estimation of Width database were used for the performance evaluation of the proposed algorithm that showed 98.1 and 97.7% accurate result for both CLRIS and VDIS, respectively. The significantly high accuracy in this newly developed vessel diameter quantification algorithm indicates excellent potential for further development, evaluation, validation, and integration into ophthalmic diagnostic instruments.

This Letter illustrates prefrontal haemodynamics as a neurovascular basis of inter-personal working memory differences. A functional near-infrared spectroscopy with sampling frequency ∼2 Hz is used to record the blood oxyhaemoglobin and deoxyhaemoglobin signals from 19 subjects engaged in working memory task of encoding and retrieval of ten symbol-meaning association learning. The individual difference in working memory performance is classified by supervised learning-based linear discriminant analysis and ensemble classifiers. Prior to the classification approach, individual performance is labelled as high, moderate and low on the basis of the performance index. The spontaneous haemodynamic activity and task-evoked responses are marked as background and foreground signals, respectively, which are scaled by means of stream-independent and stream-dependent models. The classifiers' performance shows that the stream-dependent model-based feature construction-classification improves classification accuracy to a major extent compared to the stream-independent model and no gain model. To understand the neurovascular basis of the inter-individual performance difference, diffused voxel plots are constructed. The voxel plots showed that concurrent activation of orbitofrontal and dorsolateral prefrontal cortex could have a possible association with persons' higher working memory performance.

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier – Random Forest.