NDLI: Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging

Content Provider	IEEE Xplore Digital Library
Author	Ozturk, A.E. Ceylan, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Selcuk Univ., Konya, Turkey (Ozturk, A.E.; Ceylan, M.)
Abstract	Detecting and diagnosing the liver focal lesions have vital importance in planning the treatments of the patients. While there is no need to apply any treatment for benign lesions, medical treatments or surgical operations are necessary in case of existence of malign lesions. Pre-contrast, arterial, portal venous and delayed venous phases in magnetic resonance imaging help to make clear diagnosis through their different contrast material holding properties. In this study, magnetic resonance images belonging to 60 patients are classified as benign/malign by using multi-resolution analysis methods and artificial neural networks. In proposed system, the magnetic resonance images taken from four different phases for each patient are merged with three multi-resolution analyses based on fusion rules and classified by using artificial neural networks. The accuracy rate of the study is reached to 90%.
Starting Page	415
Ending Page	419
File Size	624140
Page Count	5
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Liver Transforms Computed tomography Artificial neural networks Lesions Feature extraction ANN Multi-resolution analysis methods fusion liver classification
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

ANN based dementia diagnosis using DCT for brain MR image compression

Classification of hepatic lesions from CT images using texture features and neural networks

Fusion of MRI and PET images using retina based multi-resolution transforms

Brain imaging classification based On Learning Vector Quantization

Texture analysis based segmentation and classification of oral cancer lesions in color images using ANN

Feature selection and classification of breast MRI lesions based on multi classifier

Classification of Brain Cancer using Artificial Neural Network

Automatic Classification of Focal Lesions in Ultrasound Liver Images Using Combined Texture Features

A new approach to MRI brain images classification

Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging

Similar Documents

ANN based dementia diagnosis using DCT for brain MR image compression

Classification of hepatic lesions from CT images using texture features and neural networks

Fusion of MRI and PET images using retina based multi-resolution transforms

Brain imaging classification based On Learning Vector Quantization

Texture analysis based segmentation and classification of oral cancer lesions in color images using ANN

Feature selection and classification of breast MRI lesions based on multi classifier

Classification of Brain Cancer using Artificial Neural Network

Automatic Classification of Focal Lesions in Ultrasound Liver Images Using Combined Texture Features

A new approach to MRI brain images classification

Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging