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

Handling Multiple Instances of Symbols in Pictorial Queries by Image Similarity

. A method is presented for processing pictorial query specifications that consist of a query image and a similarity level that must hold between the query image and database images. The similarity level specifies the contextual similarity (how well does the content of one image match that of another) as well as the spatial similarity (the relative locations of the matching symbols in the two images). This method allows more than one instance of each object in the database image (while still requiring only one instance of each object in the query image). The algorithm tries to satisfy the contextual similarity first and then tries to satisfy the spatial constraints using an auxiliary graph data structure. The running time of this method is exponential in the number of objects in the query image. 1 Introduction A basic requirement of an image database is the ability to query the database pictorially. The most common method of doing this is querying via an example image. The problem wit..

Handling Multiple Instances of Symbols in Pictorial Queries by Image Similarity

. A method is presented for processing pictorial query specifications that consist of a query image and a similarity level that must hold between the query image and database images. The similarity level specifies the contextual similarity (how well does the content of one image match that of another) as well as the spatial similarity (the relative locations of the matching symbols in the two images). This method allows more than one instance of each object in the database image (while still requiring only one instance of each object in the query image). The algorithm tries to satisfy the contextual similarity first and then tries to satisfy the spatial constraints using an auxiliary graph data structure. The running time of this method is exponential in the number of objects in the query image. 1 Introduction A basic requirement of an image database is the ability to query the database pictorially. The most common method of doing this is querying via an example image. The problem wit..