NDLI: Detection of Ground Glass Opacities in Lung CT Images Using Gabor Filters and Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Hany Ayad Bastawrous Fukumoto, T. Nitta, N. Tsudagawa, M.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Ritsumeikan Univ., Shiga (Hany Ayad Bastawrous; Fukumoto, T.)
Abstract	This paper aims at developing a CAD system used for the detection of Ground Glass Opacity (GGO) nodules in chest CT images. In our scheme, we apply Gabor filter on the CT image in order to enhance the detection process. After this we perform some morphological operations including threshold process and labeling to extract the objects having high intensity values. Then, some feature analysis is used to examine these objects to decide which of them are likely to be cancer candidates. Following the feature analysis, a template matching between the potential cancer candidates and some Gaussian reference models is performed to determine the similarity between them. The algorithm was applied on 715 slices containing 25 GGO nodules and achieved detection sensitivity of 92% with False Positive (FP) rate of 0.76 FP/slice. Finally, we used an Artificial Neural Network (ANN) to reduce the number of FP findings. After using ANN, we were able to reduce the FP rate to 0.25 FP/slice but at the expense of decreasing the detection sensitivity to 84%
Starting Page	251
Ending Page	256
File Size	636383
Page Count	6
File Format	PDF
ISBN	0780388798
DOI	10.1109/IMTC.2005.1604111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Glass Lungs Computed tomography Gabor filters Neural networks Artificial neural networks Cancer Morphological operations Labeling Performance analysis Artificial Neural Networks Gabor Filters CAD Template Matching Chest CT images
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A New CAD System for Detecting Localized Ground Glass Opacity Nodules in Lung CT images Using Cross and Coronary Section Images

Artificial neural network-based classification system for lung nodules on computed tomography scans

Intelligent pulmonary embolsim detection system

Automated Extraction and Diagnosis of Lung Emphysema from Lung CT Images Using Artificial Neural Network

A Gabor filter-based classification for diffuse lung opacities in thin-section computed tomography images

Computer aided lung cancer detection system

CT Lung Image filtering based on Max-Tree method

Automatic detection of lung cancers in chest CT images by variable N-Quoit filter

Automatic lung nodules detection in computed tomography images using nodule filtering and neural networks

Detection of Ground Glass Opacities in Lung CT Images Using Gabor Filters and Neural Networks

Similar Documents

A New CAD System for Detecting Localized Ground Glass Opacity Nodules in Lung CT images Using Cross and Coronary Section Images

Artificial neural network-based classification system for lung nodules on computed tomography scans

Intelligent pulmonary embolsim detection system

Automated Extraction and Diagnosis of Lung Emphysema from Lung CT Images Using Artificial Neural Network

A Gabor filter-based classification for diffuse lung opacities in thin-section computed tomography images

Computer aided lung cancer detection system

CT Lung Image filtering based on Max-Tree method

Automatic detection of lung cancers in chest CT images by variable N-Quoit filter

Automatic lung nodules detection in computed tomography images using nodule filtering and neural networks

Detection of Ground Glass Opacities in Lung CT Images Using Gabor Filters and Neural Networks