Literature DB >> 26740861

Survey on Neural Networks Used for Medical Image Processing.

Zhenghao Shi1, Lifeng He2, Kenji Suzuki3, Tsuyoshi Nakamura4, Hidenori Itoh4.   

Abstract

This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of neural network application for medical image processing and an outlook for the future research are also discussed. By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? (2) What are the major strengths and weakness of applying neural networks for solving medical image processing tasks? We believe that this would be very helpful researchers who are involved in medical image processing with neural network techniques.

Entities:  

Keywords:  CT; Computer-aided diagnosis; Diagnosis; Image registration; Image understanding; Magnetic resonance imaging; Neural network; Object recognition; Preprocessing; Radiographic image; image segmentation; medical image processing

Year:  2009        PMID: 26740861      PMCID: PMC4699299     

Source DB:  PubMed          Journal:  Int J Comput Sci        ISSN: 1992-6669


  42 in total

1.  Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size.

Authors:  B Sahiner; H P Chan; N Petrick; R F Wagner; L Hadjiiski
Journal:  Med Phys       Date:  2000-07       Impact factor: 4.071

Review 2.  Current status and future potential of computer-aided diagnosis in medical imaging.

Authors:  K Doi
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

3.  On-line retrainable neural networks: improving the performance of neural networks in image analysis problems.

Authors:  A D Doulamis; N D Doulamis; S D Kollias
Journal:  IEEE Trans Neural Netw       Date:  2000

4.  Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study.

Authors:  M Ozkan; B M Dawant; R J Maciunas
Journal:  IEEE Trans Med Imaging       Date:  1993       Impact factor: 10.048

5.  Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach.

Authors:  Yue Wang; Tülay Adalý; Sun-Yuan Kung; Zsolt Szabo
Journal:  IEEE Trans Image Process       Date:  1998-08       Impact factor: 10.856

Review 6.  Review of neural network applications in medical imaging and signal processing.

Authors:  A S Miller; B H Blott; T K Hames
Journal:  Med Biol Eng Comput       Date:  1992-09       Impact factor: 2.602

7.  Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

Authors:  W E Reddick; J O Glass; E N Cook; T D Elkin; R J Deaton
Journal:  IEEE Trans Med Imaging       Date:  1997-12       Impact factor: 10.048

8.  Computerized tumor boundary detection using a Hopfield neural network.

Authors:  Y Zhu; H Yan
Journal:  IEEE Trans Med Imaging       Date:  1997-02       Impact factor: 10.048

9.  Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.

Authors:  H P Chan; B Sahiner; N Petrick; M A Helvie; K L Lam; D D Adler; M M Goodsitt
Journal:  Phys Med Biol       Date:  1997-03       Impact factor: 3.609

10.  Image analysis and machine learning applied to breast cancer diagnosis and prognosis.

Authors:  W H Wolberg; W N Street; O L Mangasarian
Journal:  Anal Quant Cytol Histol       Date:  1995-04       Impact factor: 0.302

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  1 in total

1.  Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies.

Authors:  Elisa Cuadrado-Godia; Pratistha Dwivedi; Sanjiv Sharma; Angel Ois Santiago; Jaume Roquer Gonzalez; Mercedes Balcells; John Laird; Monika Turk; Harman S Suri; Andrew Nicolaides; Luca Saba; Narendra N Khanna; Jasjit S Suri
Journal:  J Stroke       Date:  2018-09-30       Impact factor: 6.967

  1 in total

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