Miniature MOEMS hyperspectral imager with versatile analysis tools

The Fabry-Perot interferometers (FPI) are essential components of many hyperspectral imagers (HSI). While the Piezo-FPI (PFPI) are still very relevant in low volume, high performance applications, the tunable MOEMS FPI (MFPI) technology enables volume-scalable manufacturing, thus having potential to be a major game changer with the advantages of low costs and miniaturization. However, before a FPI can be utilized, it must be integrated with matching optical assembly, driving electronics and imaging sensor. Most importantly, the whole HSI system must be calibrated to account for wide variety of unwanted physical and environmental effects, that significantly influence quality of hyperspectral…

Discriminating Basal Cell Carcinoma and Bowen’s Disease with Novel Hyperspectral Imaging System and Convolutional Neural Networks

An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis

Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected an…

Differentiating Malignant from Benign for Melanocytic and Non-melanocytic Skin Tumors : A Pilot Study on Hyperspectral Imaging and Convolutional Neural Networks

Hexyl aminolevulinate, 5‐aminolevulinic acid nanoemulsion and methyl aminolevulinate in photodynamic therapy of non‐aggressive basal cell carcinomas: A non‐sponsored, randomized, prospective and double‐blinded trial

Background In the photodynamic therapy (PDT) of non‐aggressive basal cell carcinomas (BCCs), 5‐aminolevulinic acid nanoemulsion (BF‐200ALA) has shown non‐inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL‐PDT in the treatment of BCCs. Objectives To compare the histological clearance, tolerability (pain and post‐treatment reaction), and cosmetic outcome of MAL, BF‐200 ALA, and low‐concentration HAL in the PDT of non‐aggressive BCCs. Methods Ninety‐eight histologically verified non‐aggressive BCCs met the inclusion criter…

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

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…

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning val…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed

Minimal learning machine in anomaly detection from hyperspectral images

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set. nonPeerReviewed

FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pé…

Fourier'n sarjan suppeneminen

Funktion f Fourier'n sarja on ääretön funktiosarja, jossa summataan funktiosta f ja summausindeksistä n riippuvia Fourier'n kertoimia funktiolla e^{inx} kerrottuna. Fourier'n sarjoja käytetään esimerkiksi osittaisdifferentiaaliyhtälöiden ratkaisemiseen. Tässä tutkielmassa käsitellään Fourier'n sarjan suppenemista. Kun Fourier'n sarja keksittiin, pitkään luultiin, että jatkuvan funktion Fourier'n sarja suppenee aina. Tässä työssä osoitetaan, että näin ei ole. Ensin työssä osoitetaan, että jatkuvan funktion Fourier'n sarja "melkein suppenee," eli on Abel- ja Cesàro-summautuva. Abel-summautuvuudessa sarjan summattavat kerrotaan luvulla r^n, missä luku r on itseisarvoltaan pienempi kuin 1 ja n …

Convolutional neural networks in skin cancer detection using spatial and spectral domain

Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed

PRACTICAL APPROACH FOR HYPERSPECTRAL IMAGE PROCESSING IN PYTHON

Abstract. Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.