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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(More)
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).(More)
Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of(More)
Current diagnosis of prostatic adenocarcinoma is done by manual analysis of biopsy tissue samples for tumor presence. However, the recent advent of whole slide digital scanners has made histopathological tissue specimens amenable to computer-aided diagnosis (CAD). In this paper, we present a CAD system to assist pathologists by automatically detecting(More)
The current method of grading prostate cancer on histology uses the Gleason system, which describes five increasingly malignant stages of cancer according to qualitative analysis of tissue architecture. The Gleason grading system has been shown to suffer from interand intra-observer variability. In this paper we present a new method for automated and(More)
Distance metrics are often used as a way to compare the similarity of two objects, each represented by a set of features in high-dimensional space. The Euclidean metric is a popular distance metric, employed for a variety of applications. Non-Euclidean distance metrics have also been proposed, and the choice of distance metric for any specific application(More)
With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray(More)
In this paper we present a method of automatically detecting and segmenting glands in digitized images of prostate histology and to use features derived from gland morphology to distinguish between intermediate Gleason grades. Gleason grading is a method of describing prostate cancer malignancy on a numerical scale from grade 1 (early stage cancer) through(More)
Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing and applying computerized image analysis and multi-modal data fusion algorithms to digitized patient data (e.g. imaging, tissue, genomic) for helping physicians predict disease outcome and patient survival. While a number of(More)
Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the(More)