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The ability to accurately interpret large image scenes is often dependent on the ability to extract relevant contextual, domain-specific information from different parts of the scene. Traditionally, techniques such as multi-scale (i.e. multi-resolution) frameworks and hierarchical classifiers have been used to analyze large images. In this paper we present a novel framework that classifies entire...
Gleason grading of prostate cancer is complicated by cancer confounders, or benign tissues closely resembling malignant processes (e.g. atrophy), which account for as much as 34% of misdiagnoses. Thus, it is critical to correctly identify confounders in a computer-aided diagnosis system. In this work, we present a cascaded multi-class pairwise classifier (CascaMPa) to identify the class of regions...
Triple-negative (TN) breast cancer has gained much interest recently due to its lack of response to receptor-targeted therapies and its aggressive clinical nature. In this study, we evaluate the ability of a computer-aided diagnosis (CAD) system to not only distinguish benign from malignant lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but also to quantitatively distinguish...
The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+breast cancer (BC), making it important to study LI. The ability to detect and quantify extent of LI could serve as an image based prognostic tool for HER2+ BC patients. Lymphocyte segmentation in H & E-stained BC histopathology images is, however, complicated due to the similarity...
This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region...
In this paper we present a novel image analysis methodology for automatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, including textural and nuclear architecture based features, are extracted from a database of 48 breast biopsy tissue studies (30 cancerous and 18 benign images). Spectral clustering is used to reduce the...
Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) low- level information...
In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using...
The Cancer Biomedical Informatics Grid (caBIGtrade) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data and the use of common analytical tools. The emergence of genomics and proteomics high-throughput technologies are creating a paradigm shift in biomedical research from small independent...
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