Search results for "kuvantaminen"
showing 10 items of 59 documents
Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions
2022
Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasi…
Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and …
2022
Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface mo…
Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks
2020
New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from canc…
Intra- and Interrater Reliability of Sagittal Spinopelvic Parameters on Full-Spine Radiographs in Adults With Symptomatic Spinal Disorders
2018
Background/Aims To evaluate the intra- and interrater reliability (I-IR) of sagittal spinopelvic parameters from digital full-spine plain radiographs with basic software tools in an unselected adult population with degenerative spinal complaints who were evaluated for surgery. Methods Forty-nine adult full-spine digital radiographs were measured twice by 3 independent observers, including an experienced spine surgeon, an experienced radiologist, and a resident orthopedic surgeon. Clinical picture archiving and communication system workstations and software tools were used and landmarks were set manually. The I-IR of the sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), …
Measurement of optical second-harmonic generation from an individual single-walled carbon nanotube
2013
We show that optical second-harmonic generation (SHG) can be observed from individual single-walled carbon nanotubes (SWCNTs) and, furthermore, allows imaging of individual tubes. Detailed analysis of our results suggests that the structural noncentrosymmetry, as required for SHG, arises from the non-zero chiral angle of the SWCNT. SHG thus has potential as a fast, non-destructive, and simple method for imaging of individual nanomolecules and for probing their chiral properties. Even more, it opens the possibility to optically determine the handedness of individual SWCNTs.
Minimal learning machine in hyperspectral imaging classification
2020
A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum is classified pixel-by-pixel. In some of the real-time applications, the amount of the HS image data causes performance challenges. Those issues relate to the platforms (e.g. drones) payload restrictions, the issues of the available energy and to the complexity of the machine learning models. In this study, we introduce the minimal learning machine (MLM) as a computationally cheap training and classification machine learning method for the hyperspectral imaging classificatio…
Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks
2022
Funding Information: Funding: This research was funded by Academy of Finland ICT 2023 Smart‐HSI—“Smart hyper‐ spectral imaging solutions for new era in Earth and planetary observations” (Decision no. 335612), by the European Agricultural Fund for Rural Development: Europe investing in rural areas, Pohjois‐ Savon Ely‐keskus (Grant no. 145346) and by the European Regional Development Fund for “Cyber‐ Grass I—Introduction to remote sensing and artificial intelligence assisted silage production” pro‐ ject (ID 20302863) in European Union Interreg Botnia‐Atlantica programme. This research was car‐ ried out in affiliation with the Academy of Finland Flagship “Forest‐Human‐Machine Interplay— Buildi…
Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images
2020
Background and objective\ud Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.\ud \ud Methods\ud We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitu…
Effective elastic properties of biocomposites using 3D computational homogenization and X-ray microcomputed tomography
2021
A 3D computational homogenization method based on X-ray microcomputed tomography (μCT) was proposed and implemented to investigate how the fiber weight fraction, orthotropy and orientation distribution affect the effective elastic properties of regenerated cellulose fiber-polylactic acid (PLA) biocomposites. Three-dimensional microstructures reconstructed by means of the X-ray μCT were used as the representative volume elements (RVEs) and incorporated into the finite element solver within the computational homogenization framework. The present method used Euclidean bipartite matching technique so as to eliminate the generation of artificial periodic boundaries and use the in-situ solution d…
PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES
2021
Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyp…