Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...

self-affine maps were successfully used for edge detection, image segmentation, and contour extraction. they belong to the general category of patch-based methods. particularly, each self-affine map is defined by one pair of patches in the image domain. by minimizing the difference between these patches, the optimal translation vector of the self-affine map is obtained. almost all image process...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. thus, the need for precise segmentation of corneal layer boundaries is inevitable. obviously, manual segmentation is time‑consuming and imprecise. in this paper, the gaussian mixture model (gmm) is used for automatic segmentation of three clinically important corneal boundaries on opt...

Potato leaf disease detection in an early stage is challenging because of variations crop species, diseases symptoms and environmental factors. These factors make it difficult to detect potato the stage. Various machine learning techniques have been developed diseases. However, existing methods cannot species general these models are trained tested on images plant leaves a specific region. In t...

reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. it also decreases memory and transmission bandwidth requirements. moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. in this paper, the kohene...