The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques

Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “. In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors. Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts

Semantic Assisted, Multiresolution Image Retrieval in 3D Brain MR Volumes

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems