Ureter tracking and segmentation in CT urography (CTU) using COMPASS

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/1/mp1412_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/2/mp1412.pd

STUDY OF ERROR ESTABLISHMENT IN MILLING MACHINES WITH 5 AXES

This report examines the accuracy of rotary die processing on a 5 axes machine and a special bolt-disk system. Faults that affect the accuracy and their measurement and reduction within acceptable limits are analyzed. As a result of the measurement, a virtual model of the radial beating of the workpiece relative to the actual axis of rotation of the machine was developed

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.pd

A new automated method for the segmentation and characterization of breast masses on ultrasound images

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134986/1/mp0069.pd

An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms

Mammography is widely recognized as the most reliable technique for early detection of breast cancers. Automated or semi-automated computerized classification schemes can be very useful in assisting radiologists with a second opinion about the visual diagnosis of breast lesions, thus leading to a reduction in the number of unnecessary biopsies. We present a computer-aided diagnosis (CADi) system for the characterization of massive lesions in mammograms, whose aim is to distinguish malignant from benign masses. The CADi system we realized is based on a three-stage algorithm: a) a segmentation technique extracts the contours of the massive lesion from the image; b) sixteen features based on size and shape of the lesion are computed; c) a neural classifier merges the features into an estimated likelihood of malignancy. A dataset of 226 massive lesions (109 malignant and 117 benign) has been used in this study. The system performances have been evaluated terms of the receiver-operating characteristic (ROC) analysis, obtaining A_z = 0.80+-0.04 as the estimated area under the ROC curve.Comment: 6 pages, 3 figures; Proceedings of the ITBS 2005, 3rd International Conference on Imaging Technologies in Biomedical Sciences, 25-28 September 2005, Milos Island, Greec

Comparison of similarity measures for the task of template matching of masses on serial mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134879/1/mp1892.pd

Computerized nipple identification for multiple image analysis in computerâ aided diagnosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135606/1/mp0713.pd

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysisâ local affine transformation for improved localization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134991/1/mp6134.pd

Urinary bladder cancer staging in CT urography using machine learning

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/1/mp12510.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/2/mp12510_am.pd