Abstract
Pathological brain detection (PBD) systems help neuroradiologists to make assisted decisions based on brain images. At present, there are two types of PBD systems. Type I is aimed at detecting all types of brain disease, and its detection rate is improving gradually. Type II is aimed at detecting specific brain diseases, and then integrating all these in a system. Both types of system are hot research topics. This chapter first introduces the history of pathological brain detection. It then goes on to divide common brain diseases into four categories: neoplastic disease, neurodegeneration, cerebrovascular disease, and inflammation. We also give a standard computer-aided pathological brain detection prototype. Finally, promising research trends are predicted.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cavestri R, Radice L, D’Angelo V, Longhini E (1991) Focus. An expert system for the clinical diagnosis of the location of acute neurologic events (Focus. Un sistema esperto per la diagnosi clinica di sede di incidenti neurologici a comparsa acuta). Minerva Med 82(12):815–820
Brai A, Vibert J-F, Koutlidis R (1994) An expert system for the analysis and interpretation of evoked potentials based on fuzzy classification: application to brainstem auditory evoked potentials. Comput Biomed Res 27(5):351–366. https://doi.org/10.1006/cbmr.1994.1027
Juhola M, Auramo Y, Kentala E, Pyykko I (1995) An essay on power of expert systems versus human expertise. Med Inf 20(2):133–138.
Imran MB, Kawashima R, Sato K, Kinomura S, Ono S, Qureshy A, Fukuda H (1999) Detection of CBF deficits in neuropsychiatric disorders by an expert system: a 99Tcm-HMPAO brain SPET study using automated image registration. Nucl Med Commun 20(1):25–32. https://doi.org/10.1097/00006231-199901000-00006
Terae S, Miyasaka K, Kudoh K, Nambu T, Yoshikawa H, Shimizu T, Fujita N (1998) Wavelet compression on detection of brain lesions at MR imaging in teleradiology. In: Lemke HU, Vannier MW, Inamura K, Farman AG (eds) International Congress Series, vol 1165. International Congress Series. Elsevier Science Publishers, pp 459–463
Barra V, Boire JY (2000) Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation. JMRI–J Magn Reson Imaging 11(3):267–278. https://doi.org/10.1002/(sici)1522-2586(200003)
Antel SB, Bernasconi N, Andermann F, Bernasconi A (2003) Texture analysis lateralizes seizure focus in TLE patients with normal volumetric MRI. Epilepsia 44(Supplement 9):255
Fusco R, Sansone M, Filice S, Carone G, Amato DM, Sansone C, Petrillo A (2016) Pattern recognition approaches for breast cancer DCE-MRI classification: a systematic review. J Med Biol Eng 36(4):449–459. https://doi.org/10.1007/s40846-016-0163-7
James AP (2016) Edge detection for pattern recognition: a survey. Int J Appl Pattern Recogn 3(1):1–21. https://doi.org/10.1504/ijapr.2016.076980
Abarghouei AA, Ghanizadeh A, Sinaie S, Shamsuddin SM (2009) A survey of pattern recognition applications in cancer diagnosis. In: Abraham A, Muda AK, Herman NS, Shamsuddin SM, Huoy CY (eds), International conference of soft computing and pattern recognition, Malacca, Malaysia. IEEE, pp 448–453. https://doi.org/10.1109/socpar.2009.93
Raut O, Conrad JM, Willis AR (2011) Survey of recognition of Arabic scripts for indoor unmanned aerial vehicles using classical methods for pattern recognition. In: IEEE Southeastcon 2011: building global engineers, New York IEEE SoutheastCon-Proceedings. IEEE, pp 255–259
Bularka S, Gontean A (2015) EEG pattern recognition techniques review. In: 21st International symposium for design and technology in electronic packaging (SIITME), Brasov, Romania. IEEE, pp 273–276
van der Meer J, Frasincar F (2013) Automatic review identification on the web using pattern recognition. Softw-Pract Experience 43(12):1415–1436. https://doi.org/10.1002/spe.2152
Dorigo M (1992) Optimization, learning and natural algorithms. Politecnico di Milano, Italy
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks. IEEE, pp 1942–1948. https://doi.org/10.1109/icnn.1995.488968
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359. https://doi.org/10.1023/a:1008202821328
Karaboga D, Basturk B (2007) Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin P, Castillo O, Aguilar LT, Kacprzyk J, Pedrycz W (eds) Foundations of fuzzy logic and soft computing, vol 4529. Lecture notes in computer science. Springer-Verlag Press, Berlin, pp 789–798
Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: IEEE swarm intelligence symposium, Pasadena. IEEE, pp 84–91. https://doi.org/10.1109/sis.2005.1501606
Zhan T (2016) Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning. Prog Electromagnet Res 156:105–133
Paliogiannis P, Scognamillo F, Attene F, Marrosu A, Trignano E, Tedde L, Delogu D, Trignano M (2013) Preneoplastic and neoplastic gallbladder lesions occasionally discovered after elective video cholecystectomy for benign disease. A single centre experience and literature review. Ann Ital Chir 84(3):281–285
Martinez-Gonzalez A, Duran-Prado M, Calvo GF, Alcain FJ, Perez-Romasanta LA, Perez-Garcia VM (2015) Combined therapies of antithrombotics and antioxidants delay in silico brain tumour progression. Math Med Biol 32(3):239–262. https://doi.org/10.1093/imammb/dqu002
Vasapolli R, Schulz C, Ner DB, Heinze H, Malfertheiner P (2016) New insights into the roles of gut microbiota in neurodegenerative diseases: a systematic review. Helicobacter 21:171–171
Smeeing DPJ, Hendrikse J, Petersen ET, Donahue MJ, de Vis JB (2016) Arterial spin labeling and blood oxygen level-dependent MRI cerebrovascular reactivity in cerebrovascular disease: a systematic review and meta-analysis. Cerebrovasc Dis 42(3–4):288–307. https://doi.org/10.1159/000446081
Oostema JA, Brown MD, Reeves M (2016) Emergency department management of transient ischemic attack: a survey of emergency physicians. J Stroke Cerebrovasc Dis 25(6):1517–1523. https://doi.org/10.1016/j.jstrokecerebrovasdis.2016.02.028
Byrne ML, Whittle S, Allen NB (2016) The role of brain structure and function in the association between inflammation and depressive symptoms: a systematic review. Psychosom Med 78(4):389–400. https://doi.org/10.1097/psy.0000000000000311
Phillips P, Dong Z, Yang J (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagnet Res 152:41–58. https://doi.org/10.2528/PIER15040602
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wang, SH., Zhang, YD., Dong, Z., Phillips, P. (2018). Basics of Pathological Brain Detection. In: Pathological Brain Detection. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4026-9_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-4026-9_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4025-2
Online ISBN: 978-981-10-4026-9
eBook Packages: Computer ScienceComputer Science (R0)