NDLI: Automated lymph node detection and classification on breast and prostate cancer SPECT-CT images

Content Provider	IEEE Xplore Digital Library
Author	Papp, L. Zsoter, N. Loh, C. Ole, B. Egeler, B. Garai, I. Luetzen, U.
Copyright Year	2011
Description	Author affiliation: Medical University of Debrecen, Scanomed Ltd., Hungary (Garai, I.) \|\| Mediso Medical Imaging Systems Ltd., Baross str. 91-95, Budapest, Hungary (Papp, L.; Zsoter, N.) \|\| Medical University of Kiel, Germany (Loh, C.; Ole, B.; Egeler, B.; Luetzen, U.)
Abstract	We present a novel detection and classification method to process SPECT-CT images representing breast and prostate lymph nodes. Lymph nodes are those nodes that are near the primer tumor and may become cancerous in time, hence their early detection is a key factor for the successful treatment of the patient. Prior methods focus on the visual aid to manually detect the lymph nodes which still makes the process time-consuming. Other solutions segment the lymph nodes only on CT, where the small lymph nodes may not be located accurately. Our solution processed both SPECT and CT data to provide an accurate classification of all SPECT hot spots. The method has been validated on a huge amount of medical data. Results show that our method is a very effective tool to support physicians working with related images in the field of nuclear medicine.
Starting Page	3431
Ending Page	3434
File Size	681398
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lymph nodes Computed tomography Single photon emission computed tomography Image segmentation Breast Biomedical imaging Bones
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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