Ultrasound texture-based CAD system for detecting neuromuscular diseases

International Journal of Computer Assisted Radiology and Surgery

Volumn 10, Issue 9, 2015, Pages 1493-1503

  König, Tim ^a   Steffen, Johannes ^a   Rak, Marko ^a   Neumann, Grit ^a   von Rohden, Ludwig ^a   Tönnies, Klaus D ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Classification; Computer aided diagnosis; Neuromuscular diseases; Texture analysis; Ultrasound imaging

Indexed keywords

ADULT; AGED; ARTICLE; BICEPS BRACHII MUSCLE; CLINICAL ARTICLE; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC RADIOLOGIST; DIAGNOSTIC TEST ACCURACY STUDY; HUMAN; MYOSITIS; NEUROMUSCULAR DISEASE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REAL TIME ULTRASOUND SCANNER; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; ULTRASOUND; ALGORITHM; AUTOMATION; ECHOGRAPHY; MIDDLE AGED; NEUROMUSCULAR DISEASES; PROCEDURES; REPRODUCIBILITY; SKELETAL MUSCLE; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; AUTOMATION; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MIDDLE AGED; MUSCLE, SKELETAL; MYOSITIS; NEUROMUSCULAR DISEASES; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84941421167     PISSN: 18616410     EISSN: 18616429     Source Type: Journal    
DOI: 10.1007/s11548-014-1133-6     Document Type: Article

References (40)

1. Asvestas P, Golemati S, Matsopoulos GK, Nikita KS, Nicolaides AN (2002) Fractal dimension estimation of carotid atherosclerotic plaques from B-mode ultrasound: a pilot study. Ultrasound Med Biol 28:1129–1136
2. Awad J, Krasinski A, Parraga G, Fenster A (2010) Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images. Med Phys 37:1382–1391
3. Basset O, Sun Z, Mestas J, Gimenez G (1993) Texture analysis of ultrasonic images of the prostate by means of co-occurrence matrices. Ultrason Imaging 15:218–237
4. Basset O, Duboeuf F, Delhay B, Brusseau E, Cachard C, Tasu J (2008) Texture analysis of ultrasound liver images with contrast agent to characterize the fibrosis stage. In: Proceedings of IEEE ultrasonics symposium, pp 24–27
5. Chang RF, Wu WJ, Moon W, Chen DR (2005) Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 89:179–185
6. Chen DR, Chang RF, Huang YL (1999) Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology 213:407–412
7. Chen L, Seidel G, Mertins A (2010) Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image. In: Proceedings of international conference on image processing, pp 2277–2280
8. Christodoulou C, Pattichis C, Pantziaris M, Nicolaides A (2003) Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging 22:902–912
9. Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics
10. Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, London
11. Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2013) A MATLAB toolbox for pattern recognition. Delft Univ. Techn., http://prtools.org/, pRTools 4.2.4
12. Finette S, Bleier A, Swindell W (1983) Breast tissue classification using diagnostic ultrasound and pattern recognition techniques: I. Methods of pattern recognition. Ultrason Imaging 5:55–70
13. Good MS, Rose JL, Goldberg BB (1982) Application of pattern recognition techniques to breast cancer detection: Ultrasonic analysis of 100 pathologically confirmed tissue areas. Ultrason Imaging 4:378–396
14. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621
15. Huang Y, Han X, Tian X, Zhao Z, Zhao J, Hao D (2010) Texture analysis of ultrasonic liver images based on spatial domain methods. In: Proceedings of international congress on image signal processing, vol 2, pp 562–565
16. Huang YL, Wang KL, Chen DR (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15:164–169
17. Kier C, Seidel G, Brüggemann N, Hagenah J, Klein C, Aach T, Mertins A (2009) Transcranial sonography as early indicator for genetic Parkinson’s disease. In: European conference of the international federation for medical and biological engineering, vol 22. Springer, Berlin, pp 456–459
18. König T, Rak M, Steffen J, Neumann G, von Rohden L, Tönnies KD (2013) Texture-based detection of myositis in ultrasonographies. Proceedings of Bildverarbeitung für die Medizin (BVM), Informatik aktuell, pp 81–86
19. Layer G, Zuna I, Lorenz A, Zerban H, Haberkorn U, Bannasch P, van Kaick G, Räth U (1990) Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution. Ultrason Imaging 12:171–188
20. Lee WL, Chen YC, Hsieh KS (2003) Ultrasonic liver tissues classification by fractal feature vector based on m-band wavelet transform. IEEE Trans Med Imaging 22:382–392
21. Liao YY, Tsui PH, Li CH, Chang KJ, Kuo WH, Chang CC, Yeh CK (2011) Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. Med Phys 38:2198–2207
22. Lu NH, Kuo CM, Ding HJ (2013) Automatic ROI segmentation in B-mode ultrasound image for liver fibrosis classification. In: Proceedings of international symposium on biometrics and security technologies (ISBAST), pp 10–13
23. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693
24. Minhas FU, Sabih D, Hussain M (2012) Automated classification of liver disorders using ultrasound images. J Med Sys 36:3163–3172
25. Mohamed S, Salama M (2008) Prostate cancer spectral multifeature analysis using TRUS images. IEEE Trans Med Imaging 27:548–556
26. Ogawa K, Fukushima M, Kubota K, Hisa N (1998) Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks. IEEE Trans Nucl Sci 45:3069–3074
27. Pillen S, van Alfen N (2010) Skeletal muscle ultrasound. Eur J Transl Myol 1:145–155
28. Pohle R, Fischer D, von Rohden L (2000) Computer-supported tissue characterization in ultrasound images of neuromuscular diseases. Ultrasound Med 21:245–252
29. Reimers K, Reimers CD, Wagner S, Paetzke I, Pongratz DE (1993) Skeletal muscle sonography: a correlative study of echogenicity and morphology. J Med Ultrasound 12:73–77
30. von Rohden L, Wien F, Ptzsch S (2007) Myosonographie neuromuskulärer Erkrankungen unter besonderer Berücksichtigung des Kindes- und Jugendalters. Klin Neurophysiol 38:141–154
31. Sakalauskas A, Lukoševičius A, Laučkaite K (2011) Texture analysis of transcranial sonographic images for Parkinson disease diagnostics. Ultragarsas 66:32–36
32. Sakr AA, Fares ME, Ramadan M (2014) Automated focal liver lesion staging classification based on Haralick texture features and multi-SVM. Int J Comput Appl 91(8):17–25
33. Sheppard M, Shih L (2007) P6C-2 image texture clustering for prostate ultrasound diagnosis. In: Proceedings of IEEE ultrasonic symposium, pp 2473–2476
34. Su Y, Wang Y, Jiao J, Guo Y (2011) Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inf J 5:26–37
35. Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS (2011) Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 15:130–137
36. Webb AR (2002) Statistical pattern recognition. Wiley, New York
37. Wilhjelm J, Gronholdt ML, Wiebe B, Jespersen S, Hansen L, Sillesen H (1998) Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination. IEEE Trans Med Imaging 17:910–922
38. Wu CM, Chen YC, Hsieh KS (1992) Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging 11:141–152
39. Wu WJ, Lin SW, Moon WK (2012) Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput Med Imaging Graph 36(8):627–633
40. Zhou S, Shi J, Zhu J, Cai Y, Wang R (2013) Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomed Signal Process Control 8(6):688–696