A comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.
As a result of advances in skin imaging technology and the development of suitable image processing
techniques during the last decade, there has been a significant increase of interest in the computer-aided
diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure,
since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised
approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical
Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The
border detection error is quantified by a metric in which a set of dermatologist-determined borders is
used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods
(optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes,
dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method)
and borders determined by a second dermatologist. The results demonstrate that the presented method
achieves both fast and accurate border detection in dermoscopy images.
As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.