Christopher J. Henry

Rough Sets and Near Sets in Medical Imaging: A Review

This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

Perception‐based image classification

Purpose – The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, and to compare this method with existing image similarity measures.Design/methodology/approach – Image‐correspondence methodologies are present in many systems that are depended on daily. In these systems, the discovery of sets of similar objects (aka, tolerance classes) stems from human perception of the objects being classified. This view of perception of image‐correspondence springs directly from Poincares work on visual spaces during 1890s and Zeemans work on tolerance spaces and visual acuity during 1960s. Thus, in solving the image‐correspondence problem, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of digital images (and perceptual objects, in general) based on features that describe...

Image Pattern Recognition Using Near Sets

The problem considered in this paper is how to recognize similar objects based on the detection of patterns in pairs of images. This article introduces a new form of classifier based on approximation spaces in the context of near sets for use in pattern recognition. By way of introducing the basic approach, nonlinear diffusion is used for edge detection and object contour extraction. This form of image transformation makes it possible to compare the contours of objects in pairs of images. Once the contour of an image has been identified, it is then possible to construct approximation spaces based on vectors of probe function measurements associated with selected image features. In this article, the only feature considered is contour, which leads to many contour probe functions. The contribution of this article is a new form of classifier, based on approximation spaces, for use in image pattern recognition.

Rough Ethograms: Study of Intelligent System Behavior

This article introduces a new form of ethogram that provides a basis for studying reinforcement learning in biologically inspired collective robotics systems. In general, an ethogram is a record of behavior patterns, which has grown out of ethology (ways to explain agent behavior). The rough set approach introduced by Zdzislaw Pawlak in 1982 provides a ground for deriving pattern-based rewards in the context of an approximation space. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards used to compute action rewards as well as action preferences. A brief description of a prototype of an ecosystem testbed used to record ethograms in a dynamically changing system of agents is presented. The contribution of this article is an introduction to an ethological approach to the study of action preferences and action rewards during reinforcement learning in intelligent systems considered in the context of approximation spaces.

Reinforcement learning in swarms that learn

This paper introduces an approach to reinforcement learning by cooperating agents using a variation of the actor critic method. This is made possible by considering behavior patterns of swarms in the context of approximation spaces. Rough set theory introduced by Zdzislaw Pawlak in 1982 provides a ground for deriving pattern-based rewards within approximation spaces. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards used to estimate action preferences. Approximation spaces are used to derive action-based reference rewards at the swarm intelligence level. Two different forms of the actor critic reinforcement learning method are considered as a part of a study of learning in real-time by a swarm. The contribution of this article is the presentation of a new actor critic method defined in the context of approximation spaces. An ecosystem designed to facilitate study of reinforcement learning by swarms is briefly described. In addition, the results of ecosystem experiments for two forums of the actor critic method are given.

Parallel computation in finding near neighbourhoods

The problem considered in this article stems from the observation that practical applications of near set theory requires efficient determination of all the tolerance classes containing objects from the union of two disjoints sets. Near set theory consists in extracting perceptually relevant information from groups of objects based on their descriptions. Tolerance classes are sets where all the pairs of objects within a set must satisfy the tolerance relation and the set is maximal with respect to inclusion. Finding such classes is a computationally complex problem, especially in the case of large data sets or sets of objects with similar features. The contribution of this article is a parallelized algorithm for finding tolerance classes using NVIDIAs Compute Unified Device Architecture (CUDA). The parallelized algorithm is illustrated in terms of a content-based image retrieval application.

Signature-based Perceptual Nearness: Application of Near Sets to Image Retrieval

This paper presents a signature-based approach to quantifying perceptual nearness of images. A signature is defined as a set of descriptors, where each descriptor consists of a real-valued feature vector associated with a digital image region (set of pixels) combined with a region-based weight. Tolerance near sets provide a formal framework for our application of near sets to image retrieval. The tolerance nearness measure tNM was created to demonstrate application of near set theory to the problem of image correspondence. A new form of tNM has been introduced in this work, which takes into account the region size. Our method is compared to two other well-known image similarity measures: earth movers distance (EMD) and integrated region matching (IRM).

Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis

The problem considered in this paper is how to discern and compare similarities in perceptually indiscernible objects in visual rough sets that are disjoint. The solution to the problem stems from the introduction of probe functions, object description, near set theory, perceptual systems, and perceptual indiscernibility relations. This leads to a new form of image analysis.

Perceptual image analysis

The problem considered in this paper is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are qualitatively represented by feature-value vectors containing probe function values computed in a manner similar to feature extraction in pattern classification theory. The work presented here is a generalisation of a solution to extracting perceptual information from images using near sets theory which provides a framework for measuring the perceptual nearness of objects. Further, near set theory is used to define a perception-based approach to image analysis that is inspired by traditional mathematical morphology and an application of this methodology is given by way of segmentation evaluation. The contribution of this article is the introduction of a new method of unsupervised segmentation evaluation that is base on human perception rather than on properties of ideal segmentations as is normally the case.

Maximal clique enumeration in finding near neighbourhoods

The problem considered in this article stems from the observation that practical applications of near set theory require efficient determination of all the tolerance classes containing objects from the union of two disjoints sets. Near set theory consists in extracting perceptually relevant information from groups of objects based on their descriptions. Tolerance classes are sets where all the pairs of objects within a set must satisfy the tolerance relation and the set is maximal with respect to inclusion. Finding such classes is a computationally complex problem, especially in the case of large data sets or sets of objects with similar features. The contributions of this article are the observation that the problem of finding tolerance classes is equivalent to the MCE problem, empirical evidence verifying the conjecture from [15] that the extra perceptual information obtained by finding all tolerance classes on a set of objects obtained from a pair of images improves the CBIR results when using the tolerance nearness measure, and a new application of MCE to CBIR.