We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surf...

This paper compares the KH and KJ sign images in the context of the zero-thresholding at single and multiple scales. It points out that consistent zero-thresholding of curvatures remains necessary for multiple scale surface segmentation. Even though KJ sign image is a good choice for single scale surface segmentation with a zero-thresholding formula J = K , the KH sign image is a better choice ...

Abstract— This paper presents a review of wavelet thresholding techniques for despeckling of medical ultrasound images. An ultrasound image is first transformed into wavelet domain and then the wavelet coefficients are processed by different wavelet thresholding techniques. The denoised image is obtained by taking the inverse wavelet transform of the modified wavelet coefficients. The performan...

Noise reduction has been a traditional problem in image processing. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequency signal details. However, the local space-scale information of the image is not adaptively considered by standard wavelet thresholding methods. In this paper, a new type of thresh...

The paper presents two techniques of image segmentation to facilitate image edge detection, that can be used further by image analysis based on the extracted features of image edges, Canny edge detection and Otsu thresholding are examples of the proposed techniques, the paper evaluates the effectiveness of the two methods with a variety of images, testing their suitability to natural as well as...

— Image denoising is an important step in image compression and other image processing algorithms. Hard and soft thresholding algorithms are often used to denoise the images. Recently wavelet transform has been used as a tool to denoise the images. However, there are problems associated with the thresholding algorithms. There is no subjective way to determine the threshold. In this work, we imp...

We propose a fast pairwise nearest neighbor (PNN)based O(N log N) time algorithm for multilevel nonparametric thresholding, where N denotes the size of the image histogram. The proposed PNN-based multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8 to 16 bits-per-pixel real images, experimental results also reveal that the proposed method provides be...

Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing...

Multilevel thresholding is a method that is widely used in image segmentation. The thresholding problem is treated as an optimization problem with an objective function. In this article, a simple and histogram based approach is presented for multilevel thresholding in image segmentation. The proposed method combines Tsallis objective function and Particle Swarm Optimization (PSO). The PSO algor...

In this study we conduct an exhaustive survey of image thresholding methods, categorize them and express their formulae under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as: histogram shape, measurement space clustering, entropy, object attributes, spatial correlation and loca...