NDLI: Use of neural networks for feature based recognition of liver region on CT images

Content Provider	IEEE Xplore Digital Library
Author	Afaq Husain, S. Shigeru, E.
Copyright Year	2000
Description	Author affiliation: Dept. of Syst. Sci., Kyoto Univ., Japan (Afaq Husain, S.)
Abstract	Medical diagnostic support systems are gaining popularity due to easy access of computers in this field and the increasing workload of radiologists. The automatic processing of X-ray images, segregation of different regions, and detection of certain features are a few of the objectives of this task. Neural networks have been successfully applied to various pattern recognition problems. High-resolution images, such as X-ray computed tomography (CT) images, where real time processing is desirable, present a challenge to image processing. Neural networks, due to their parallel processing nature, present an attractive prospect to the solution of such images. Since these medical images require a priori knowledge for their analysis, knowledge is stored in the network through training. A neural network has been trained to learn the texture of the liver region that may be used in 3D rendering and automatic segmentation of normal and abnormal liver regions. The network is based on a backpropagation neural network (BPNN) that is trained on a set of features calculated in a window of fixed pixel size. The system learns the texture of the liver region through supervised training and gives acceptable results for an independent set of images not used during training.
Starting Page	831
Ending Page	840
File Size	558196
Page Count	10
File Format	PDF
ISBN	0780362780
ISSN	10893555
DOI	10.1109/NNSP.2000.890163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-12-11
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Liver X-ray imaging Computed tomography Medical diagnosis X-ray detection X-ray detectors Computer vision Pattern recognition Image processing
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

Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method

Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system

Computer assisted diagnosis of lung cancer using helical X-ray CT

Computer assisted diagnosis of lung cancer using helical X-ray CT

Physico-Mathematical Considerations on X-Ray Computed Tomography Based on Diffraction Enhanced Imaging

Medical image recognition of abdominal X-ray CT images by RBF GMDH-type neural network

Automated recognition of the psoas major muscles on X-ray CT images

Enhancement approach for liver lesion diagnosis using unenhanced CT images

An algorithm for coronary calcification diagnosis based on helical CT images

Use of neural networks for feature based recognition of liver region on CT images

Similar Documents

Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method

Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system

Computer assisted diagnosis of lung cancer using helical X-ray CT

Computer assisted diagnosis of lung cancer using helical X-ray CT

Physico-Mathematical Considerations on X-Ray Computed Tomography Based on Diffraction Enhanced Imaging

Medical image recognition of abdominal X-ray CT images by RBF GMDH-type neural network

Automated recognition of the psoas major muscles on X-ray CT images

Enhancement approach for liver lesion diagnosis using unenhanced CT images

An algorithm for coronary calcification diagnosis based on helical CT images

Use of neural networks for feature based recognition of liver region on CT images