Feasibility of Haralick's Texture Features for the Classification of Chromogenic In-situ Hybridization Images

This paper presents a proof of concept for the usefulness of second-order texture features for the qualitative analysis and classification of chromogenic in-situ hybridization whole slide images in high-throughput imaging experiments. The challenge is that currently, the gold standard for gene expression grading in such images is expert assessment. The idea of the research team is to use different approaches in the analysis of these images that will be used for structural segmentation and functional analysis in gene expression. The article presents such perspective idea to select a number of textural features that are going to be used for classification. In our experiment, natural grouping of image samples (tiles) depending on their local texture properties was explored in an unsupervised classification procedure. The features are reduced to two dimensions with fuzzy c-means clustering. The overall conclusion of this experiment is that Haralick features are a viable choice for classification and analysis of chromogenic in-situ hybridization image data. The principal component analysis approach produced slightly more "understandable" from an annotator's point of view classes.Comment: 4 pages, 1 figur

Fuzzy C-Means and Gath-Geva Methods in Clustering Districts Based on Human Development Indeks (HDI) in South Sulawesi

District grouping in South Sulawesi based on the Human Development Index (HDI) indicators needs to be done as a material for planning and evaluating the targets of government work programs. This grouping is based on dominant indicators of the high and low HDI. The value of the HDI indicator needs to be considered so that the achievement of each indicator is known. Statistical analysis that can be used to group districts that have similarities is cluster analysis. The method that is currently developing is fuzzy clustering analysis, which classifies objects using certain membership degrees. Fuzzy clustering algorithm that can be used is Fuzzy C-means (FCM). Another method of fuzzy clustering analysis developed further is Gath Geva (GG), which is able to detect groups with different forms. In this study, the fuzzy clustering process on the FCM and GG methods with the same parameters and shows that the GG method is better than the FCM method. This conclusion is based on a total of 1000 iterations. The GG method gives an objective function value smaller than FCM, besides it gives a faster- conferencing iteration result

Quality indices for (practical) clustering evaluation

WOS:000271584000004 (Nº de Acesso Web of Science)Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters' compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution

An extendable general platform for cluster analysis and validation

This thesis implements a general platform which integrates several commonly used cluster analysis algorithms with ease of use and extendability, along with various validation techniques to analyze the results of algorithms

Clustering of fMRI data: the elusive optimal number of clusters

Model-free methods are widely used for the processing of brain fMRI data collected under natural stimulations, sleep, or rest. Among them is the popular fuzzy c-mean algorithm, commonly combined with cluster validity (CV) indices to identify the ‘true’ number of clusters (components), in an unsupervised way. CV indices may however reveal different optimal c-partitions for the same fMRI data, and their effectiveness can be hindered by the high data dimensionality, the limited signal-to-noise ratio, the small proportion of relevant voxels, and the presence of artefacts or outliers. Here, the author investigated the behaviour of seven robust CV indices. A new CV index that incorporates both compactness and separation measures is also introduced. Using both artificial and real fMRI data, the findings highlight the importance of looking at the behavior of different compactness and separation measures, defined here as building blocks of CV indices, to depict a full description of the data structure, in particular when no agreement is found between CV indices. Overall, for fMRI, it makes sense to relax the assumption that only one unique c-partition exists, and appreciate that different c-partitions (with different optimal numbers of clusters) can be useful explanations of the data, given the hierarchical organization of many brain networks