Markku Hauta-Kasari

Spectral vision system for measuring color images

We present a spectral vision system that can be used to measure a color spectrum and two-dimensional spectral images. First, a low-dimensional color filter set was designed by an unsupervised neural network. Then a compact optical setup for the spectral synthesizer was constructed to synthesize the light that corresponds to the spectral characteristics of the color filter. In the optical setup a liquid-crystal spatial light modulator was used to implement color filters. A sample was illuminated by the synthesized lights, and the intensity images that correspond to the inner products between the color filter and the sample were detected by a CCD camera. From the detected inner products the sample’s color spectra were reconstructed by use of a pseudoinverse matrix. Experimental results of measuring a single color spectrum and spectral images are presented.

Wiener estimation method in estimating of spectral reflectance from RGB images

Color is one of the most important features in digital images. The representation of color in digital form with a three-component image (RGB) is not very accurate, hence the use of a multiple-component spectral image is justified. At the moment, acquiring a spectral image is not as easy and as fast as acquiring a conventional three-component image. One answer to this problem is to use a regular digital RGB camera and estimate its RGB image into a spectral image by the Wiener estimation method, which is based on the use of a priori knowledge. In this paper, the Wiener estimation method is used to estimate the spectra of icons. The experimental results of the spectral estimation are presented.

Generalized co-occurrence matrix for multispectral texture analysis

We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM). In the spatial domain we use a generalized co-occurrence matrix for vector valued pixels. The texture feature vectors are classified by the k-nearest neighbor (KNN) classifier and the multilayer perceptron (MLP) network. Experimental results of the multispectral texture segmentation are presented.

Regularized learning framework in the estimation of reflectance spectra from camera responses

For digital cameras, device-dependent pixel values describe the cameras response to the incoming spectrum of light. We convert device-dependent RGB values to device- and illuminant-independent reflectance spectra. Simple regularization methods with widely used polynomial modeling provide an efficient approach for this conversion. We also introduce a more general framework for spectral estimation: regularized least-squares regression in reproducing kernel Hilbert spaces (RKHS). Obtained results show that the regularization framework provides an efficient approach for enhancing the generalization properties of the models.

Multi-spectral Texture Segmentation Based on the Spectral Cooccurrence Matrix

Abstract: Multi-spectral images are becoming more common in industrial inspection tasks where the colour is used as a quality measure. In this paper we propose a spectral cooccurrence matrix-based method to analyse multi-spectral texture images, in which every pixel contains a measured colour spectrum. We first quantise the spectral domain of the multi-spectral images using the Self-Organising Map (SOM). Next we label the spectral domain according to the quantised spectra. In the spatial domain, we represent a multi-spectral texture using the spectral cooccurrence matrix, which we calculate from the labelled image. In the experimental part of this paper, we present the results of segmenting natural multi-spectral textures. We compared the k-nearest neighbour (k-NN) classifier and the multilayer perceptron (MLP) neural network-based segmentation results of the multi-spectral and RGB colour textures.

Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition

This study investigates the relationship between the optical response of human articular cartilage in the visible (VIS) and near infrared (NIR) spectral range and its matrix properties.Full-thickness osteochondral cores (dia. = 16 mm, n = 50) were extracted from human cadaver knees (N = 13) at four anatomical locations and divided into quadrants. Absorption spectra were acquired in the spectral range 400-1100 nm from one quadrant. Reference biomechanical, biochemical composition, histological, and cartilage thickness measurements were obtained from two other quadrants. A multivariate statistical technique based on partial least squares (PLS) regression was then employed to investigate the correlation between the absorption spectra and tissue properties.Our results demonstrate that cartilage optical response correlates with its function, composition and morphology, as indicated by the significant relationship between spectral predicted and measured biomechanical (79.0%  ⩽  R(2)  ⩽  80.3%, p  <  0.0001), biochemical (65.1%  ⩽  R(2)  ⩽  81.0%, p  < 0.0001), and histological scores ([Formula: see text] = 83.3%, p  < 0.0001) properties. Significant correlation was also obtained with the non-calcified cartilage thickness ([Formula: see text] = 83.2%, p  <  0.0001).We conclude that optical absorption of human cartilage in the VIS and NIR spectral range correlates with the overall tissue properties, thus providing knowledge that could facilitate development of systems for rapid assessment of tissue integrity.

Nonnegative Tensor Factorization Accelerated Using GPGPU

This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speedups measured on real spectral images are around 60 - 100× compared to a traditional C implementation compiled with an optimizing compiler. Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speedup achieved using a graphics card is attractive. The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and engineers using NTF on large problems.

Optical spectral imaging of degeneration of articular cartilage.

Osteoarthritis (OA) is a common musculoskeletal disorder often diagnosed during arthroscopy. In OA, visual color changes of the articular cartilage surface are typically observed. We demonstrate in vitro the potential of visible light spectral imaging (420 to 720 nm) to quantificate these color changes. Intact bovine articular cartilage samples (n=26) are degraded both enzymatically using the collagenase and mechanically using the emery paper (P60 grit, 269 microm particle size). Spectral images are analyzed by using standard CIELAB color coordinates and the principal component analysis (PCA). After collagenase digestion, changes in the CIELAB coordinates and projection of the spectra to PCA eigenvector are statistically significant (p<0.05). After mechanical degradation, the grinding tracks could not be visualized in the RGB presentation, i.e., in the visual appearance of the sample to the naked eye under the D65 illumination. However, after projecting to the chosen eigenvector, the grinding tracks are revealed. The tracks are also seen by using only one wavelength, i.e., 469 nm, however, the contrast in the projection image is 1.6 to 2.5 times higher. Our results support the idea that the spectral imaging can be used for evaluation of the integrity of the cartilage surface.

Highlight Removal from Single Image

The highlight removal method from the single image without knowing the illuminant has been presented. The presented method is based on the Principal Component Analysis (PCA), Histogram equalization and Second order polynomial transformation. The proposed method does not need color segmentation and normalization of image by illuminant. The method has been tested on different types of images, images with or without texture and images taken in different unknown light environment. The result shows the feasibility of the method. Implementation of the method is straight forward and computationally fast.

Optical spectral reflectance of human articular cartilage – relationships with tissue structure, composition and mechanical properties

The information from spectral reflectance of articular cartilage has been related to the integrity of the tissue. This study explores more in detail the interrelations between the cartilage composition, structure and mechanical properties, and optical spectral reflectance. Using human osteochondral samples the reflectance spectral images of articular cartilage were captured and analyzed by using CIELAB color space as well as principal component analysis. With both analysis methods statistically significant correlations were observed between the reflectance and histological integrity, as assessed by Mankin scoring, tissue proteoglycan content and dynamic modulus. In thick human cartilage, the reflectance was found to be independent of the cartilage thickness, suggesting negligible influence of the underlying subchondral bone. Based on the present results diagnostically relevant information on cartilage quality can be extracted using optical spectral reflectance measurements. These measurements could be feasible during arthroscopic surgery when more in-depth information of the properties of articular cartilage is needed.