Computer aided detection in mammography

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Computer-aided diagnostic systems for digital mammograms

A computer-aided diagnostic (CAD) system that uses a unique shape-based classification scheme, the Ellipse-Closed Curve Fitting (ECCF) algorithm, is developed for digital mammogram image analysis. The system is developed to work as a post-processing extension to a previously developed CAD system that locates and segments mass lesions, or tumors, found in digital mammograms into separate images. The ECCF system is implemented in the MATLAB mathematical scripting language and is thus capable of running on multiple platforms. The ECCF algorithm detects edges in tumor images and casts them into closed curve functions. Parameters for an ellipse of best fit for a closed curve function are computed in a way analogous to that in linear regression, where a line of best fit is determined to fit a set of data points. In addition to the shape-fitting algorithm, the ECCF system comprises several other independently functioning components, including auxiliary algorithms and techniques that perform image cropping and edge detection, employed initially to prepare the images for efficient processing, and self-test tools that calculate R2, area matching ratios, and a shape conformity value to determine the goodness of fit . Output generated by the ECCF system for sufficiently large image sets may contain correlations between malignant tumors and their shape that may be captured with data mining techniques, the implementation of which may result in an improved integrated CAD system

Anatomy Segmentation of Breast Ultrasound images

Breast cancer is one of the most common cancers in women, affecting hundreds of women. Even though the detection of cancer has been largely studied, the decision of which strategy to take concerning oncoplastic surgery still relies almost exclusively on the surgeon's perception of post-surgical aesthetic result, which sometime leads to unsatisfactory outcomes. In order to empower the patients on the joint decision process there needs to exist a better communication between the parts. This can be achieved by developing medical grade 3D models of the breast and explaining better the surgical options and their results. In order to obtain such models, some effort has been made concerning multi-modality radiological imaging combination. This line of research has yet to mature. In turn, the modality alignment requires accurate landmarks to be produced. 2D Ultrasound imaging has not been sufficiently studied for multimodal registration due to the image characteristics and thus, landmark segmentation is of utmost importance. This task can be challenging since US data presents high specular noise levels and the presence of some tissues alters the perception of other tissues. Objectives: ● Study and evaluation of different techniques for anatomical landmark segmentation, such as Skin, Fat and Glandular tissue, Lesions (masses and cysts), Pectoral muscle; ● Development of Ultrasound segmentation methods for acquiring landmarks; ● Evaluation of the developed methods with manual annotations and comparison of results with the current algorithm alternatives.Breast cancer is one of the most common cancers in women, affecting hundreds of women. Even though the detection of cancer has been largely studied, the decision of which strategy to take concerning oncoplastic surgery still relies almost exclusively on the surgeon's perception of post-surgical aesthetic result, which sometime leads to unsatisfactory outcomes. In order to empower the patients on the joint decision process there needs to exist a better communication between the parts. This can be achieved by developing medical grade 3D models of the breast and explaining better the surgical options and their results. In order to obtain such models, some effort has been made concerning multi-modality radiological imaging combination. This line of research has yet to mature. In turn, the modality alignment requires accurate landmarks to be produced. 2D Ultrasound imaging has not been sufficiently studied for multimodal registration due to the image characteristics and thus, landmark segmentation is of utmost importance. This task can be challenging since US data presents high specular noise levels and the presence of some tissues alters the perception of other tissues. Objectives: ● Study and evaluation of different techniques for anatomical landmark segmentation, such as Skin, Fat and Glandular tissue, Lesions (masses and cysts), Pectoral muscle; ● Development of Ultrasound segmentation methods for acquiring landmarks; ● Evaluation of the developed methods with manual annotations and comparison of results with the current algorithm alternatives

A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations

Background: It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach. Results: We have designed and implemented GraphPAC (Graph Protein Amino Acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC identifies clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html Conclusion: GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.Comment: 25 pages, 8 figures, 3 Table