Dynamic contrast-enhanced magnetic resonance (DCE-MR) of the breast is especially robust for the diagnosis of cancer in high-risk women due to its high sensitivity. Its specificity may be, however, compromised since several benign masses take up contrast agent as malignant lesions do. In this paper, we propose a novel method of 3D multifractal analysis to characterize the spatial complexity (spatial arrangement of texture) of breast tumors at multiple scales. Self-similar properties are extracted from the estimation of the multifractal scaling exponent for each clinical case, using lacunarity as the multifractal measure. These properties include several descriptors of the multifractal spectra reflecting the morphology and internal spatial structure of the enhanced lesions relatively to normal tissue. The results suggest that the combined multifractal characteristics can be effective to distinguish benign and malignant findings, judged by the performance of the support vector machine classification method evaluated by receiver operating characteristics with an area under the curve of 0.96. In addition, this paper confirms the presence of multifractality in DCE-MR volumes of the breast, whereby multiple degrees of self-similarity prevail at multiple scales. The proposed feature extraction and classification method have the potential to complement the interpretation of the radiologists and supply a computer-aided diagnosis system.

Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income.

Glaucoma is a heterogeneous group of diseases characterised by cupping of the optic nerve head and visual field damage, starting with a progressive loss of vision that leads to permanent blindness. When diagnosed in time, Glaucoma can be delayed by adequate treatment. More efficient processes for diagnosis are being proposed, and the role of artificial intelligence in the field is growing. This work presents a pipeline to evaluate the impact of generative modelling in Computer-Aided Diagnosis (CADx) of Glaucoma based on Deep Learning, particularly focused on the optic disc region. The methodology relies on transforming retinal fundus images to improve and degrade their quality to augment the training data and assess the diagnostic performance. The objective evaluation of the proposed model based on Generative Adversarial Networks revealed quantitative and qualitative improvements in image quality. To support this, we propose a new model to evaluate the quality of fundus images, which can also be used within the pipeline to reject samples with lower image quality for diagnosis. Its performance surpassed related work, achieving a balanced accuracy of 0.929. Concerning Glaucoma CADx, the results obtained in public datasets point to a considerable gain in Sensitivity, Specificity, and Accuracy, achieving scores of 0.883 (+0.054), 0.957 (+0.019), and 0.931 (+0.031), respectively, after image data augmentation when compared with previous work targeted at offline inference in mobile devices. Considering the restriction of choosing simpler backbone networks that can run on edge devices, our findings support the importance of image quality diversity and realistic augmentation.

Improving inventory management is essential to retailer profitability. This paper proposes a supervised learning approach for Out-of-Stock (OOS) detection by Texture, Color and Geometry features in high-resolution panoramic images of grocery retail shelves. Cascade classifiers are used to detect labels that can potentially be used to confirm the presence of the OOS cases. The image acquisition setup includes a camera cart that shoots from multi-viewpoints aiming a parallel motion to the shelf. The correction of perspective distortion is applied to handle the different camera translation motions while stitching together images with a high-level of similarity. From the generated panoramas, the proposed OOS detection is followed by classification with Support Vector Machines. The experimental tests were performed throughout the retail environment with real data obtained from supermarket shelves containing labels near the visible ruptures. Results show a detection accuracy of 84.5% for OOS and a sensitivity of 86.6% for label detection.

This paper proposes a multiscale automated model for the classification of suspicious malignancy of breast masses, through log detrended fluctuation cumulant-based multifractal analysis of images acquired by dynamic contrast-enhanced magnetic resonance. Features for classification are extracted by computing the multifractal scaling exponent for each of the 70 clinical cases and by quantifying the log-cumulants reflecting multifractal information related with texture of the enhanced lesions. The output is compared with the radiologist diagnosis that follows the Breast Imaging-Reporting and Data System (BI-RADS). The results suggest that the log-cumulant C ₂ can be effective in classifying typically biopsy-recommended cases. The performance of a supervised classification was evaluated by receiver operating characteristic (ROC) with an area under the curve of 0.985. The proposed multifractal analysis can contribute to novel feature classification techniques to aid radiologists every time there is a change in the clinical course, namely, when biopsy should be considered.