Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification

Ovarian Neoplasm Imaging

Volumn , Issue , 2013, Pages 425-440

  Rajendra Acharya, U ^a   Mookiah, Muthu Rama Krishnan ^a   Vinitha Sree, S ^b   Yanti, Ratna ^a   Martis, Roshan ^a   Saba, Luca ^c   Molinari, Filippo ^d   Guerriero, Stefano ^e   Suri, Jasjit S ^f,g  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b Global Biomedical Technologies Inc   (United States)

^c DEPARTMENT OF RADIOLOGY   (Italy)

^d POLITECNICO DI TORINO   (Italy)

^e UNIVERSITY OF CAGLIARI   (Italy)

^f Global Biomedical Technologies   (United States)

^g Nucl Eng Idaho State University   (United States)

Author keywords

Computer aided diagnosis; Entropy; Gabor transform; Hu s invariant moments; Ovarian cancer; Probabilistic neural network

Indexed keywords

AUTOMATION; DATA HANDLING; DATA MINING; DISEASES; ENTROPY; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; MEDICAL COMPUTING; NEURAL NETWORKS; PARAMETER ESTIMATION; TUMORS; ULTRASONIC APPLICATIONS;

CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSTICS; CROSS-VALIDATION TECHNIQUE; GABOR TRANSFORM; MALIGNANT DISEASE; OVARIAN CANCERS; PROBABILISTIC NEURAL NETWORKS; S-INVARIANT;

COMPUTER AIDED DIAGNOSIS;

EID: 84903988574     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4614-8633-6_27     Document Type: Chapter

References (44)

1. Jemal A, Siegel R, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010;60:277–300.
2. NIH Consensus Development Panel on Ovarian Cancer. NIH consensus conference. Ovarian cancer. Screening, treatment, and follow-up. JAMA. 1995;273:491–7.
3. Horner MJ, Ries LAG, Krapcho M, Neyman N, Aminou R, Howlader N, Altekruse SF, Feuer EJ, Huang L, Mariotto A, Miller BA, Lewis DR, Eisner MP, Stinchcomb DG, Edwards BK, editors. SEER cancer statistics review, 1975–2006, National Cancer Institute, Bethesda. SEER Website. seer.cancer.gov/csr/1975_2006. Based on November 2008 SEER data submission. Published 29 May 2009.
4. Predanic M, Vlahos N, Pennisi JA, Moukhtar M, Alee FA. Color and pulsed Doppler sonography, gray-scale imaging, and serum CA 125 in the assessment of adnexal disease. Obstet Gynecol. 1996;88:283–8.
5. Wu CC, Lee CN, Chen TM, Lai JI, Hsieh CY, Hwieh FJ. Factors contributing to the accuracy in diagnosing ovarian malignancy by color Doppler ultrasound. Obstet Gynecol. 1994;84:605–8.
6. Iyer VR, Lee SI. MRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterization. AJR Am J Roentgenol. 2010;194:311–21.
7. Sohaib SA, Reznek RH. MR imaging in ovarian cancer. Cancer Imaging. 2007;7 Spec No A:S119–29.
8. Frangioni JV. New technologies for human cancer imaging. J Clin Oncol. 2008;26:4012–21.
9. Anderiesz C, Quinn MA. Screening for ovarian cancer. Med J Aust. 2003;178:655–6.
10. Jeong YY, Outwater EK, Kang HK. Imaging evaluation of ovarian masses. Radiographics. 2000;20:1445–70.
11. Pascual MA, Graupera B, Hereter L, Rotili A, Rodriguez I, Alcázar JL. Intra-and interobserver variability of 2D and 3D transvaginal sonography in the diagnosis of benign versus malignant adnexal masses. J Clin Ultrasound. 2011;39:316–21.
12. Guerriero S, Alcazar JL, Pascual MA, Ajossa S, Gerada M, Bargellini R, Virgilio B, Melis GB. Intraobserver and interobserver agreement of greyscale typical ultrasonographic patterns for the diagnosis of ovarian cancer. Ultrasound Med Biol. 2008;34: 1711–6.
13. Kim KA, Park CM, Lee JH, Kim HK, Cho SM, Kim B, Seol HY. Benign ovarian tumors with solid and cystic components that mimic malignancy. AJR Am J Roentgenol. 2004;182:1259–65.
14. Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems. Ultrasonics. 2012; 52:508–20.
15. Saba L, Gao H, Acharya UR, Sannia S, Ledda G, Suri JS. Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model. Neuroradiology. 2012;54(11):1207–14. PubMed PMID: 22562690.
16. Renz C, Rajapakse JC, Razvi K, Liang SKC. Ovarian cancer classification with missing data. In: Proceedings of 9th international conference on Neural Information Processing, Singapore, 2002, vol. 2, p. 809–13.
17. Assareh A, Moradi MH. Extracting efficient fuzzy if-then rules from mass spectra of blood samples to early diagnosis of ovarian cancer. In: IEEE symposium on Computational Intelligence and Bioinformatics and Computational Biology, Honolulu, 2007, p. 502–6.
18. Tan TZ, Quek C, Ng GS, Razvi K. Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med. 2008;43:207–22.
19. Meng H, Hong W, Song J, Wang L. Feature extraction and analysis of ovarian cancer proteomic mass spectra. In: 2nd international conference on Bioinformatics and Biomedical Engineering, Shanghai, 2008, p. 668–71.
20. Tang KL, Li TH, Xiong WW, Chen K. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010;11:109.
21. Petricoin F. Use of proteomic patterns serum to identify ovarian cancer. The Lancet. 2002;359:572–7.
22. Tailor A, Jurkovic D, Bourne TH, Collins WP, Campbell S. Sonographic prediction of malignancy in adnexal masses using an artificial neural network. Br J Obstet Gynaecol. 1999; 106:21–30.
23. Brüning J, Becker R, Entezami M, Loy V, Vonk R, Weitzel H, Tolxdorff T. Knowledge-based system ADNEXPERT to assist the sonographic diagnosis of adnexal tumors. Methods Inf Med. 1997;36:201–6.
24. Biagiotti R, Desii C, Vanzi E, Gacci G. Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US. Radiology. 1999;210:399–403.
25. Zimmer Y, Tepper R, Akselrod S. An automatic approach for morphological analysis and malignancy evaluation of ovarian masses using B-scans. Ultrasound Med Biol. 2003;29:1561–70.
26. Lucidarme O, Akakpo JP, Granberg S, Sideri M, Levavi H, Schneider A, Autier P, Nir D, Bleiberg H, Ovarian HistoScanning Clinical Study Group. A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study. Eur Radiol. 2010;20:1822–30.
27. Bellman RE. Dynamic programming. Mineola: Courier Dover Publications; 2003.
28. Hata T, Yanagihara T, Hayashi K, Yamashiro C, Ohnishi Y, Akiyama M, Manabe A, Miyazaki K. Three-dimensional ultrasonographic evaluation of ovarian tumours: a preliminary study. Hum Reprod. 1999;14:858–61.
29. Laban M, Metawee H, Elyan A, Kamal M, Kamel M, Mansour G. Three-dimensional ultrasound and three-dimensional power Doppler in the assessment of ovarian tumors. Int J Gynaecol Obstet. 2007;99:201–5.
30. Cohen LS, Escobar PF, Scharm C, Glimco B, Fishman DA. Three-dimensional power Doppler ultrasound improves the diagnostic accuracy for ovarian cancer prediction. Gynecol Oncol. 2001;82:40–8.
31. Hu M. Visual pattern recognition by moment invariants. IRE Trans Info Theory. 1962;8:179–87.
32. Shen L, Bai L. A review of Gabor wavelets for face recognition. Patt Anal Appl. 2006;9:273–92.
33. Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell. 1996;18: 837–42.
34. Pharwaha APS, Singh B. Shannon and non-shannon measures of entropy for statistical texture feature extraction in digitized mammograms. Proceedings of the World Congress on Engineering and Computer Science (WCECS). San Francisco, USA. 2009, Vol 2. p. 2179.
35. Box JF. Guinness, gosset, fisher, and small samples. Statist Sci. 1987;2:45–52.
36. Specht DF. Probabilistic neural networks. Neural Networks. 1990;3:109–18.
37. Raghu PP, Yegnanarayana B. Supervised texture classification using a probabilistic neural network and constraint satisfaction model. IEEE Trans Neural Netw. 1998;9:516–22.
38. Ng EYK, Acharya UR, Keith LG, Lockwood S. Detection and differentiation of breast cancer using neural classifiers with first warning thermal sensors. Inform Sciences. 2007;177:4526–38.
39. Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading: Addison Wesley Professional Publishers, Boston, MA, USA. 1989.
40. Deb K. Multi-objective optimization using evolutionary algorithms. Chichester/New York: Wiley; 2009.
41. Bast Jr RC, Badgwell D, Lu Z, Marquez R, Rosen D, Liu J, Baggerly KA, Atkinson EN, Skates S, Zhang Z, Lokshin A, Menon U, Jacobs I, Lu K. New tumor markers: CA125 and beyond. Int J Gynecol Cancer. 2005;15:274–81.
42. Zaidi SI. Fifty years of progress in gynecologic ultrasound. Int J Gynaecol Obstet. 2007;99:195–7.
43. Menon U, Talaat A, Rosenthal AN, Macdonald ND, Jeyerajah AR, Skates SJ, Sibley K, Oram DH, Jacobs IJ. Performance of ultrasound as a second line test to serum CA125 in ovarian cancer screening. BJOG. 2000;107:165–9.
44. Acharya UR, Sree SV, Krishnan MRM, Saba L, Molinari F, Guerriero S, Suri JS. Ovarian tumor characterization using 3D ultrasound. Technol Cancer Res Treat. 2012;11(6):543–52.