Joni Hyttinen

University of Eastern Finland | UEF · School of Computing

Standard algorithms face difficulties when learning from unbalanced datasets because they are built to handle balanced class distributions. Although there are various approaches to solving this issue, solutions that create false data represent a more all-encompassing strategy than algorithmic changes. In particular, they produce fictitious data tha...

The dataset consists of 101 hyperspectral images of four human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma,...

Hyperspectral imaging (HSI) is an emerging modal-ity in medical imaging. Large-scale clinical utilization of HSI warrants data sets for learning and dedicated classification algorithms, which are currently under research and development. In this work, we present a new unsupervised segmentation method for HSI data of human placenta tissues. The prop...

Article Unsupervised Spectral Analysis of Bio-Dyed Textile Samples Zong-Yue Li 1,*, Joni Hyttinen 1, Riikka Räisänen 2, Xiao-Zhi Gao 1, and Markku Hauta-Kasari 1 1 School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, 80101, Kuopio, 70211, Finland 2 Department of Education, Faculty of Educational...

Background: Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical H...

Clinically interesting low-contrast dental and oral features can be challenging to detect. In visual observation and clinical photographs, identification of low-contrast features can be hard or even impossible. Imaging methods, e.g., X-ray and magnetic resonance imaging, provide more information but often require use of ionizing radiation, expensiv...

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medica...

In optical imaging, optical filters can be used to enhance the visibility of features-of-interest and thus aid in visualization. Optical filter design based on hyperspectral imaging employs various statistical methods to find an optimal design. Some methods, like principal component analysis, produce vectors that can be interpreted as filters that...

The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imag...

Dental lesions such as calculus and initial caries can be challenging to distinguish in RGB colour images due to a poor contrast. The visibility of dental lesions can be improved by using spectrally optimised light sources. In this paper, the optimal spectral shapes of illuminants for the visibility enhancement of various lesions are determined. Th...

Spectral imaging provides an image of a target over a relatively large number of wavelength bands. With the advances in imaging technology, spectral imaging is becoming increasingly popular in different areas of research. However, as spectral images typically contain more than three wavelength bands, visualization of the spectral data is often chal...