Examples from living systems at various levels of the biological hierarchy and also from natural food products show that ultra-weak photon emission (UPE) has potential applications in the rating of vital functions and quality testing. In this study, the UPE of chicken eggs has been tested regarding the possibility of egg quality verification. The UPE from intact eggs and separated egg parts were subjected to supervised and unsupervised classification methods according to different housing types. The results of unsupervised egg grouping substantially agreed with the types of hen rearing. The Cohen’s Kappa test score for the K-means method was up to K=0.63. Supervised Support Vector Machine (SVM) classifier with radial kernel function achieved a relatively high accuracy (AC), up to 88%, also confirmed by the value of the K-statistics up to 0.81. This study shows that the best result of egg types classification can be obtained using UPE emission data from all egg parts.

In this paper the problem of segmentation of volumetric medical images is considered.The fast and effective segmentation is obtained by applying the proposed approach which combines the idea of supervoxels and the Fuzzy C-Means algorithm.In particular, Fuzzy C-Means is used to cluster supervoxels produced by the fast 3D region growing.Additional acceleration of the method is achieved with the support of graphical processor (GPU).The detailed description of the proposed approach is given.The results of applying the method to volumetric CT and MRI brain images and CT images of various phantoms are presented, analysed and discussed.The issues related to accuracy of the method, memory workload and the running time are also considered.

This paper considers the problem of image segmentation using the random walker algorithm. In the case of 3D images, the method uses an extreme amount of memory and time resources. These are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations, this paper proposes techniques for the optimization of the random walker approach. The optimization is obtained by processing supervoxels representing homogeneous image regions rather than single voxels. A fast and efficient method for supervoxel determination is introduced. A method for the creation of an image adjacency graph from an irregular grid of supervoxels is also proposed. The results of applying the introduced approach to segmentation of 3D CT data sets are presented and compared with the results of the original random walker approach and other state-of-the-art methods. The accuracy and the computational overhead is regarded in the comparison. The analysis of results shows that the modified method can be successfully applied for the segmentation of volumetric images and provides results in a reasonable time without a significant loss in the image segmentation accuracy. It also outperforms the state-of-the-art methods considered in the comparison.

In this paper problem of graph based image segmentation is considered. In particular, attention is paid to minimal spanning tree based algorithm proposed by Felzenszwalb and Huttenlocher (FH). Although the method yields high quality results for various classes of images, its application is limited mainly to off‐line processing. Its due to the very long execution time of the FH method, which in the case of high resolution images, requires processing of millions of vertices and edges contained within the resulting graph. Therefore, some improvements to the FH method are proposed in this paper. The modifications aim at the reduction of algorithm execution time and the usage of computer host memory. These goals are achieved both by reducing the size of input image graph and by applying the methods of GPU parallel computing at initial stages of the algorithm. As the reduction of graph size is obtained by processing meta‐pixels representing homogenous regions, the new method is most suitable for the segmentation of images including rare, structurally complex objects distributed over nonuniform background. Results obtained by the introduced approach are compared with the results of the original FH method and other popular graph‐based approaches to image segmentation. The comparison includes both the accuracy of image segmentation and the execution time. Analysis of the results clearly shows, that the proposed approach in many cases can significantly accelerate segmentation process without a noticeable loss of image segmentation quality.