skin cancer has been the most usual and illustrates 50% of all new cancers detected each year. if they detected at an early stage, treatment can become simple and economically. accurate skin lesion segmentation is important in automated early skin cancer detection and diagnosis systems. the aim of this study is to provide an effective approach to detect the skin lesion border on a purposed imag...

A performance analysis procedure that analyses the properties of a class of iterative image thresholding algorithms is described. The image under consideration is modeled as consisting of two maximum-entropy primary images, each of which has a quasi-Gaussian probability density function. Three iterative thresholding algorithms identified to share a common iteration architecture are employed for...

Image thresholding is a common task in many computer vision and graphics applications. The goal of thresholding an image is to classify pixels as either “dark” or “light”. Adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination. We present a technique for real-time adaptive thresholding using the integral image of the input. Our technique is an...

Image segmentation is a basic work in the field of image analysis and computer vision. Thresholding is one of the simplest methods of image segmentation. In general, thresholding approaches based on 1-D histogram do not make use of any space adjacent information of the image, thus it is often ruined by noise; thus, thresholding methods based on 2-D histogram are put forward. These methods have ...

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and twodimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-...

Image thresholding is a necessary task in some image processing applications. However, due to disturbing factors, e.g. non-uniform illumination, or inherent image vagueness, the result of image thresholding is not always satisfactory. In recent years, various researchers have introduced new thresholding techniques based on fuzzy set theory to overcome this problem. Regarding images as fuzzy set...

Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion func...

Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are intr...

The performance of a number of image processing methods depends on the output quality of a thresholding process. Typical thresholding methods are based on partitioning pixels in an image into two clusters. In this paper, a new thresholding method is presented. The main contribution of the proposed approach is the application of the empirical mode decomposition (EMD) on detecting an optimal thre...