NDLI: The innovation 2d chest image segmentation for identification of tuberculosis by graph cut method

Content Provider	IEEE Xplore Digital Library
Author	Shiny, R.N. Chandra, B.V. Athinarayanan, S.
Copyright Year	2015
Description	Author affiliation: PSN Coll. of Eng. & Technol., Tirunelveli, India (Shiny, R.N.; Chandra, B.V.; Athinarayanan, S.)
Abstract	Tuberculosis (TB) is the second leading cause of death from an infectious disease worldwide, after HIV. TB is an infectious disease caused by the bacillus Mycobacterium tuberculosis, which typically affects the lungs. Several antibiotics exist for treating TB. While mortality rates are high when left untreated, treatment with antibiotics greatly improves the chances of survival. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high and diagnosing tuberculosis still remains a challenge. An automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs is proposed. First it extracts the lung region using a graph cut segmentation method. For this lung region, a set of texture and shape features are computed, which enable the X-rays to be classified as normal or abnormal using a binary classifier. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts.
Starting Page	1
Ending Page	7
File Size	366386
Page Count	7
File Format	PDF
ISBN	9781479917525
e-ISBN	9781479917532
DOI	10.1109/ICSNS.2015.7292429
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Histograms Shape Lungs Computational modeling Reinforcement learning Feature extraction Markov Decision Process Cross-layer strategies Biomedical imaging
Content Type	Text
Resource Type	Article

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