Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial ...

a, High magnification confocal image of Kv4.2 immunostaining in the VPM of mouse thalamus. b, Segmentation of Kv4.2 immunopositive objects by Moran’s (magenta outlines). c, Segmentation of ...

The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to ...

For the image dataset of the aggregated image dataset obtained after aggregation of the two image datasets of apple ring rot and apple anthracnose, when Lesion segmentation method 1 was used, the mean ...

High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM ...

The genetic algorithm (GA) is a metaheuristic that have been successfully applied to several practical optimization problems. The theoretical foundation of GA is based on the mechanisms of ...

Image segmentation using the EM algorithm that relies on a GMM for intensities and a MRF model on the labels. Based on "Segmentation of brain MR images through a hidden Markov random field model and ...

In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the ...

3.1. Principle of Image Segmentation Based on Dual Population Genetic Algorithm The evolutionary process of nature is the process of continuous iteration of the basic factors of reproduction, ...