Abstract
The creation of 3D complete models of the woman breast that aggregate radiological and surface information is a crucial step for the development of surgery planning tools in the context of breast cancer. This requires the registration of interior and surface data of the breast, which has to recover large breast deformations caused by the different poses of the patient during data acquisition and has to deal with the lack of landmarks between both modalities, apart from the nipple. In this paper, the registration of Magnetic Resonance Imaging exams and 3D surface data reconstructed from Kinect (TM) acquisitions is explored using a biomechanical modelling of breast pose transformations combined with a free form deformation to finely match the data. The results are promising, with an average euclidean distance between the matched data of 0.81 +/- 0.09 mm being achieved.

Abstract
Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient's breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice. (C) 2020 The Authors. Published by Elsevier Ltd.

Abstract
Overuse of antibiotics is causing the environment to become polluted with them. This is a major threat to global health, with bacteria developing resistance to antibiotics because of it. To monitor this threat, multiple antibiotic detection methods have been developed; however, they are normally complex and costly. In this work, an affordable, easy to use alternative based on digital colourimetry is proposed. Photographs of samples next to a colour reference target were acquired to build a dataset. The algorithm proposed detects the reference target, based on binarisation algorithms, in order to standardise the collected images using a colour correction matrix converting from RGB to XYZ, providing a necessary colour constancy between photographs from different devices. Afterwards, the sample is extracted through edge detection and Hough transform algorithms. Finally, the sulfonamide concentration is estimated resorting to an experimentally designed calibration curve, which correlates the concentration and colour information. Best performance was obtained using Hue colour, achieving a relative standard deviation value of less than 3.5%. © 2019, Springer Nature Switzerland AG.

Abstract

Abstract
The misuse and overuse of antibiotics lead to antibiotic resistance becoming a serious problem and a threat to world health. Bacteria developing resistance results in more dangerous infections and a more difficult treatment. To monitor the antibiotic pollution of environmental waters, different detection methods have been developed, however these are normally complex, costly and time-consuming. In a previous work, we developed a method based on digital colorimetry, using smartphone cameras to acquire sample images and color correction to ensure color constancy between images. A reference chart with 24 colors, with known ground truth values, is included in the photographs in order to color correct the images using least squares minimization. Then, the color of the sample is detected and correlated to antibiotic concentration. Although achieving promising results, the method was too sensitive to contrasting illumination conditions, with high standard deviations in these cases. Here, we test different methods for improving the stability and precision of the previous algorithm. By using only the 13 patches closest to the color of the targets and more parameters for the least squares minimization, better results were achieved, with an improvement of up to 83.33% relative to the baseline. By improving the color constancy, a more precise, less influenced by extreme conditions, estimation of sulfonamides is possible, using a practical and cost-efficient method. © 2021, Springer Nature Switzerland AG.