Abstract This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre‐segmented with super‐pixels, followed by feature extraction using colour information of each super‐pixels. The resulting graph consists of vertices which represent super‐pixels, whereas the edge weights are a measure of similarity between super‐pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state‐of‐the‐art methods, produce better results in terms of segmentation quality and processing time.

We introduce an interactive method for retina layer segmentation in gray-level and RGB images based on super-pixels, multi-level optimization of modularity, and boundary erosion. Our method produces highly accurate segmentation results and can segment very large images. We have evaluated our method with two datasets of 2D confocal microscopy (CM) images of a mammalian retina.We have obtained average Jaccard index values of 0.948 and 0.942 respectively, confirming the high-quality segmentation performance of our method relative to a known ground truth segmentation. Average processing time was two seconds.

Image segmentation has many applications which range from machine learning to medical diagnosis. In this study, the authors propose a framework for the segmentation of images based on super‐pixels and algorithms for community identification in graphs. The super‐pixel pre‐segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches based on spectral graph partition. The authors also compared their method with two algorithms: (i) the graph‐based approach by Felzenszwalb and Huttenlocher and (ii) the contour‐based method by Arbelaez. Results have shown that their method provides more precise segmentation and is faster than both of them.

Classification is an important task for computer-aided diagnosis systems (CADs). However, many classifiers may not perform well, presenting poor generalization and high computational cost, especially when dealing with high-dimensional datasets. Thus, feature selection can greatly mitigate these problems. In this paper, we propose two filter-based feature selection algorithms that calculate the simplified silhouette statistic as evaluation function: the silhouette-based greedy search (SiGS) and the silhouette-based genetic algorithm search (SiGAS). Silhouette statistic is used to guide the search for features that provide better class separability. Experiments performed on three datasets have shown that the SiGAS algorithm overcomes traditional filter algorithms, such as CFS, FCBF and reliefF. It also outperforms a similar algorithm, kNNGAS, based on genetic algorithm that minimizes the classification error of k-nearest neighbors. Additionally, results have shown that SiGAS produces better accuracy than SiGS.

The ranking problem is a crucial task in the information retrieval systems. In this paper, we take advantage of single valued ranking evaluation functions in order to develop a new method of genetic feature selection tailored to improve the accuracy of content-based image retrieval systems. We propose to boost the feature selection ability of the genetic algorithms (GA) by employing an evaluation criteria (fitness function) that relies on order-based ranking evaluation functions. The evaluation criteria are provided by the GA and has been successfully employed as a measure to evaluate the efficacy of content-based image retrieval process, improving up to 22% the precision of the query answers. Experiments on three medical datasets containing breast cancer diagnosis and breast tissue density analysis showed that fitness functions based on ranking evaluation functions occupy an essential role on the algorithms' performance, obtaining results significatively better than other fitness function designs. The experiments also showed that the proposed method obtains results superior than feature selection based on the traditional decision-tree C4.5, naive bayes, support vector machine, 1-nearest neighbor and association rule mining.

This paper introduces the concept of digital planar surfaces and corresponding Morse operators. These operators offer a novel and powerful method for construction and de-construction of such surfaces in a way that global topological control of the resulting object is always maintained. In that respect, this paper offers a complete pixel characterization tool. Image handling is a natural application for such approach. We present a novel fast algorithm for image segmentation using Morse operators for digital planar surfaces. It classifies as a region growing technique with added topological control and is extremely useful for applications that need proper object description. Results from real data are stimulating, and show that the segmentation algorithm compares very well with other methods. The topological approach also forms a base for future expansion to applications such as volume segmentation.