Automatic segmentation of skin cancer images using adaptive color clustering

This paper presents the development of an adaptive image segmentation algorithm designed for the identification of the skin cancer and pigmented lesions in dermoscopy images. The key component of the developed algorithm is the Adaptive Spatial K-Means (A-SKM) clustering technique that is applied to extract the color features from skin cancer images. Adaptive-SKM is a novel technique that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The A-SKM has been included in the development of a flexible color-texture image segmentation scheme and the experimental data indicates that the developed algorithm is able to produce accurate segmentation when applied to a large number of skin cancer (melanoma) images

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition

Pattern Classification of Human Epithelial Images

This project shows an important role to diagnosis autoimmune disorder which is by a comparative analysis on the most appropriate clustering technique for the segmentation and also to develop algorithm for positivity classification. In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. First of all, image enhancement will take part in order to boost efficiency of algorithm by implementing some of the adjustment and filtering technique to increase the visibility of image. After that, the second stage will be the image segmentation by using most appropriate clustering technique. There will be a comparative analysis on clustering techniques for segmentation which are adaptive fuzzy c-mean and adaptive fuzzy moving k-mean. Then, for features extraction, by calculating the mean of each of the properties such as area, perimeter, major axis length, and minor axis length for each images. After that, will implementing a grouping based on properties dataset that has been calculated. Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage

New machine-learning-based techniques for DNA microarray image segmentation.

Microarray technology, which provides detailed and abundant information about biological experiments, is a significant achievement in the history of biology. One of the key issues in the microarray processing is to extract quantitative information from the spots, which represent the genes in the experiments. The process of identifying the spots and separating the foreground from the background is known as microarray image segmentation. In this thesis, we present two methods for microarray image segmentation. First, we conduct an in-depth analysis of the influence of important factors on clustering-based microarray image segmentation algorithms. Based on our analysis, we present an optimized clustering-based algorithm for microarray image segmentation, which exploits more than one feature to gain better results comparing to the state-of-the-art clustering-based algorithms. We also consider the fact that most of the spots in a microarray image are ellipses in shape, and hence introduce a novel adaptive ellipse method. This method shows various advantages when compared to the adaptive circle method, one of the most used approaches in microarray image segmentation. The simulations on real-life microarray images show that our method is capable of extracting information from the images which is ignored by the traditional adaptive circle method, and hence showing more flexibility. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Q26. Source: Masters Abstracts International, Volume: 43-03, page: 0887. Adviser: Luis Rueda. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Performance characterization of clustering algorithms for colour image segmentation

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms