Similarity Classification and Retrieval in Cancer Images and Informatics

Techniques in image similarity, classification, and retrieval of breast cancer images and informatics are presented in this thesis. Breast cancer images in the mammogram modality have a lot of non-cancerous structures that are similar to cancer, which makes them especially difficult to work with. Only the cancerous part of the image is relevant, so the techniques must learn to recognize cancer in noisy mammograms and extract features from that cancer to classify or retrieve similar images. There are also many types or classes of cancer with different characteristics over which the system must work. Mammograms come in sets of four, two images of each breast, which enables comparison of the left and right breast images to help determine relevant features and remove irrelevant features. Image feature comparisons are used to create a similarity function that works well in the high-dimensional space of image features. The similarity function is learned on an underlying clustering and then integrated to produce an agglomeration that is relevant to the images. This technique diagnoses breast cancer more accurately than commercial systems and other published results. In order to collect new data and capture the medical diagnosis used to create and improve these methods, as well as develop relevant feedback, an innovative image retrieval, diagnosis capture, and multiple image viewing tool is presented to fulfill the needs of radiologists. Additionally, retrieval and classification of prostate cancer data is improved using new high-dimensional techniques like dimensionally-limited distance functions and dimensional choice

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