Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first previous next last
cannonical link button

http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-90cd3128-7e04-4c0c-86bc-464d2c85bb68

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Computer aided diagnosis system for abdomen diseases in computed tomography images

Autorzy Sethi, G.  Saini, B. S. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In this paper, a computer aided diagnostic (CAD) system for classification of abdomen diseases from computed tomography (CT) images is presented. The methodology used in this paper is to select the most appropriate machine learning technique of segmentation, feature extraction and classification for each module of proposed CAD. The methodology of selecting appropriate machine learning technique for each module of CAD results in accurate and efficient system. Regions of interest are segmented from CT images of tumor, cyst, calculi and normal liver using active contour models, region growing and thresholding. The CAD presented in this research work exploits the discriminating power of features for classifying abdominal diseases. Therefore, feature extraction module extracts statistical texture descriptors using three kinds of feature extraction methods i.e. Gray-Level co-occurrence matrices (GLCM), Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT). At the next stage, effective and optimum features of ROIs are selected using Genetic Algorithm (GA). Further, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to assess the capability of features for classification of diseases of abdomen. The study is performed on 120 CT images of abdomen (30 normal, 30 tumor, 30 cyst and 30 calculi). It is observed from the results that proposed CAD consists of edge based active contour model combined with optimized statistical texture descriptors using DCT along with ANN as classifier achieves the best diagnostic performance of 95.1%. It is also shown in results that proposed CAD achieves highest sensitivity, specificity of 95% and 98% respectively.
Słowa kluczowe
PL sztuczna sieć neuronowa   diagnostyka wspierana komputerowo   dyskretna transformata falkowa   algorytm genetyczny   segmentacja obrazu  
EN artificial neural network   computer aided diagnosis   discrete curvelet transform   discrete wavelet transform   genetic algorithm   image segmentation  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 42--55
Opis fizyczny Bibliogr. 40 poz., rys., tab., wykr.
Twórcy
autor Sethi, G.
autor Saini, B. S.
  • Electronics and Communication Engineering Department, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar 144011, Punjab, India
Bibliografia
[1] Bankmann N. Handbook of medical imaging. New York: Academic; 2000.
[2] Kononenk I. Machine learning for medical diagnosis. History, state of the art and perspective. Artif Intell Med 2001;23:89–109.
[3] Chen E, Chung P, Chen C, Tsai H, Chang I. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 1998;45:783–94.
[4] Gletsos M, Mougiakakou G, Matsopoulos K, Nikita S, Nikita A, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions design and optimization of a neural network classifier. IEEE Trans Inf Technol Biomed 2003;7:153–62.
[5] Huang Y, Chen J, Shen W. Computer-aided diagnosis of liver tumours in non-enhanced CT images. J Med Phys 2004;9:141–50.
[6] Huang Y, Chen J, Shen W. Diagnosis of hepatic tumours with texture analysis in non enhanced computed tomography images. Acad Radiol 2006;13: 713–20.
[7] Dettori L, Semler L. A comparison of wavelet, ridgelet, and curvelet based texture classification algorithm in computed tomography. Comput Biol Med 2007;37:486–98.
[8] Dua S, Acharya U, Chowriappa P, Vinitha S. Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inf Technol Biomed 2012;16:80–7.
[9] Kumar S, Moni R, Rajeesh J. An automatic computer-aided diagnosis system for liver tumours on computed tomography images. Comput Electr Eng 2013;39: 1516–26.
[10] Li C, Xu C, Gui C, Fox M. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 2010;19:3243–54.
[11] Zhang K, Zhang L, Song Z. Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput 2010;28:668–76.
[12] Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1927;3:610–21.
[13] Pham L, Xu C, Prince J. Current methods in image segmentation. Annu Rev Biomed Eng 2000;2:315–37.
[14] Sethian: Level sets methods and fast marching methods: evolving interfaces in computational geometry in fluid mechanics, computer vision and material science. Cambridge University Press; 19999780521645577.
[15] Haralick R, Yokoyama R. Texture synthesis using a growth model. Comput Graph Image Process 1978;8:369–81.
[16] Julesz B. Textons: the elements of texture perception and their interactions. Nature 1987;290:91–7.
[17] Koenderink JJ. The structure of images Biological cybernetics, vol. 50. 1984;p. 363–70.
[18] Daubechies I. Wavelet transforms and orthonormal wavelet bases. Different perspectives on wavelets. Proceedings of Symposia in Applied Mathematics; 1993. p. 1–33.
[19] Mallat S. A theory for multi resolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 1989;11:674–93.
[20] Candes E, Donoho D. Curvelets: multi-resolution representation and scaling laws wavelet applications in signal and image processing. SPIE 2000;41:19–101.
[21] Karahaliou N, Boniatis S, Skiadopoulos G, Sakellaropoulos N, Arikidis S, Likaki A. Breast cancer diagnosis: analyzing texture of tissue surrounding micro calcifications. IEEE Trans Inf Technol Biomed 2008;12:731–8.
[22] Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng 2014;40:16–28.
[23] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157–82.
[24] Kharrat A, Messaoud M, Benamrane N, Mariem A. Genetic Algorithm for feature selection of MR brain images using wavelet cooccurence. Proc. International Conference on Signal and Information Processing. 2010. pp. 606–10.
[25] Goldberg D. Genetic algorithms in search and optimization. 1st ed. Boston: Addison-Wesley; 1989.
[26] Whitley D, Starkweather T, Bogart C. Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 1990;14:347–61.
[27] Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.
[28] Burges C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2:121–67.
[29] Schölkopf B. Statistical learning and kernel methods. Springer; 2001.
[30] Duan K, Keerthi S. Which is the best multiclass SVM method? In: Oza NC, editor. An empirical study MCS Lecture notes in computer science, vol. 3541. 2005. p. 278–85.
[31] Lee Y, Lee C. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 2003;19(9):1132–9.
[32] Paliwal M, Kumar U. Neural networks and statistical techniques: a review of applications. Expert Syst Appl 2009;36(1):2–17.
[33] Haykin S. Neural networks: a comprehensive foundation. Prentice-Hall; 1999.
[34] Ahmad F, Ashidi N, Hussain Z, Osman N. Intelligent medical disease diagnosis using improved hybrid genetic algorithm – multilayer perceptron network. J Med Syst 2013;37:9934.
[35] Rand WM. Objective criteria for the evaluation of clustering methods, vol. 66. American Statistical Association; 1971. p. 846–50.
[36] Unnikrishnan R, Hebert M. Measures of similarity. Seventh IEEE Workshop on Computer Vision Applications. 2005. pp. 394–400.
[37] Meilă M. Comparing clustering – an axiomatic view. Proc International Conference on Machine Learning. 2005. pp. 577–84.
[38] Chai H. Performance metric for active contour models in image segmentation. Int J Phys Sci 2011;6:6329–41.
[39] Dogra D, Majumdar A, Sural S. Evaluation of segmentation techniques using region area and boundary matching information. J Vis Commun Image 2012;23:150–60.
[40] Martin D, Fowlkes C, Tal D, Malik J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceeding of ICCV. 2001. pp. 416–25.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-90cd3128-7e04-4c0c-86bc-464d2c85bb68
Identyfikatory
DOI 10.1016/j.bbe.2015.10.008