Abstract
This paper summarizes the presentations given in the special ICMB2010 session on state-of-the-art of computer-aided detection/diagnosis (CAD). The topics are concerned with the latest development of technologies and applications in CAD, which include brain MR images, fundus photographs, dynamic chest radiography, chest CT images, whole breast ultrasonography, CT colonography and torso FDG-PET scans.
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References
Arimura, H., Magome, T., Yamashita, Y., et al.: Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2(3), 925–952 (2009)
Arimura, H., Li, Q., Korogi, Y., et al.: Computerized detection of intracranial aneurysms for 3D MR angiography: Feature extraction of small protrusions based on a shape-based difference image technique. Medical Physics 33(2), 394–401 (2006)
Uchiyama, Y., Yokoyama, R., Ando, H., et al.: Computer-aided diagnosis scheme for detection of lacunar infarcts on MR images. Academic Radiology 14, 1554–1561 (2007)
Arimura, H., Yoshiura, Kumazawa, S., et al.: Automated method for identification of patients with Alzheimer’s disease based on three-dimensional MR images. Academic Radiology 15(3), 274–284 (2008)
Kawata, Y., Arimura, H., Yamashita, Y., et al.: Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging. Computerized Medical Imaging and Graphics (in press, 2010)
Yamamoto, D., Arimura, H., Kakeda, S., et al.: Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Computerized Medical Imaging and Graphics (in press, 2010)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19(3), 203–210 (2000)
Zhang, L., Li, Q., You, J., Zhang, D.: Modified matched filter with double-side thresholding and its application to prolifetive diabetic retinopathy screening. IEEE Trans. on Information Technology in Biomedicine 13(4), 528–534 (2009)
Takada, E., Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Endo, T., Morita, T.: Semi-Automatic Ultrasonic Full-Breast Scanner and Computer-Assisted Detection System for Breast Cancer Mass Screening. In: Proc. of SPIE Medical Imaging 2007: Ultrasonic Imaging and Signal Processing, vol. 6513, pp. 651310-1–651310-8. SPIE, Bellingham (2007)
Lee, G.N., Morita, T., Fukuoka, D., Ikedo, Y., Hara, T., Fujita, H., Takada, E., Endo, T.: Differentiation of Mass Lesions in Whole Breast Ultrasound Images: Volumetric Analysis. In: Radiological Society of North America. In: 94th Scientific Assembly and Annual Meeting Program, p. 456 (2008)
Lee, G.N., Fukuoka, D., Ikedo, Y., Hara, T., Fujita, H., Takada, E., Endo, T., Morita, T.: Classification of Benign and Malignant Masses in Ultrasound Breast Image Based on Geometric and Echo Features. In: Krupinski, E.A. (ed.) IWDM 2008. LNCS, vol. 5116, pp. 433–440. Springer, Heidelberg (2008)
Fukuoka, D., Hara, T., Fujita, H., Endo, T., Kato, Y.: Automated Detection and Classification of Masses on Breast Ultrsonograms and its 3D Imaging Technique. In: Yaffe, M.J. (ed.) IWDM 2000: 5th International Workshop on Digital Mammography, pp. 182–188. Medical Physics, Madison (2001)
Fukuoka, D., Hara, T., Fujita, H.: Detection, Characterization, and Visualization of Breast Cancer Using 3D Ultrasound Images. In: Suri, J.S., Rangayyan, R.M. (eds.) Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, pp. 557–567. SPIE, Bellingham (2006)
Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Takada, E., Endo, T., Morita, T.: Development of a Fully Automatic Scheme for Detection of Masses in Whole Breast Ultrasound Images. Med. Phys. 34(11), 4378–4388 (2007)
Summers, R.M., et al.: Automated polyp detection at CT colonography: Feasibility assessment in the human population. Radiology 219, 51–59 (2001)
Morson, B.C.: The polyp-cancer sequence in the large bowel. Proc. R. Soc. Med. 67, 451–457 (1974)
Taylor, S.A., et al.: CT colonography: computer-aided detection of morphologically flat T1 colonic carcinoma. European Radiology 18, 1666–1673 (2008)
Iinuma, G., et al.: The Challenge: Detection of Early-Stage Superficial Colorectal lesions. Virtual Colonoscopy - A Practical Guid
Hara, T., Kobayashi, T., Kawai, K., Zhou, X., Ito, S., Katafuchi, T., Fujita, H.: Automated scoring system of standard uptake value for torso FDG-PET. In: Proc. of SPIE Medical Imaging 2008: Computer-aided diagnosis, vol. 6915, pp. 691534-1 – 691534-4 (2008)
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Fujita, H. et al. (2010). State-of-the-Art of Computer-Aided Detection/Diagnosis (CAD). In: Zhang, D., Sonka, M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13923-9_32
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DOI: https://doi.org/10.1007/978-3-642-13923-9_32
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