An Information Recognition System for Complex Images
Abstract An approach to objective assessment of ultrasound examination is presented. To this end, modern information technologies and a set of mathematical methods in the form of a package are proposed. In this paper, diagnosis is viewed as a three-step process, and closed sub-objects are investigated using complex images, which pertains to the earliest diagnostic stage. For this purpose, three new features related to the disclosure of a growth are included in the paper. A system that performs the detection of the growth and finds the coordinates, area, gravity center and color palette of the obtained image is developed. By means of the created software package, the image is cleared from noise, filtering operations are performed, boundaries are defined more clearly and recognition by the mathematical morphology method is completed using selected classifiers. The main purpose is to direct doctor's attention to the presence of the pre-indicator of a non-specific symptom and to control the future development of the growth. The accuracy of the system is confirmed by the detection and identification of closed growths in the images taken in an ultrasound examination of internal organs of the human body. The system's operability has been tested directly on the ultrasound images (138 cases investigated), with the result of 98.8% at the diagnostic stage, 92, 03% at the early diagnostic stage; 2 cases have been recorded at the earliest diagnostic stage in 2018 and the frequency of monitoring has been determined.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abdullaeva, G.G. & Alizadeh, U.M. (2015). Recognition of complex images on a plane. Scientific journal “Achievements and problems of modern science”, 65-70.
Abdullayeva, G.G. & Kazim-Zada, A.K. Recognition and identification of Plane Color Images in the Case of Carpet Designs. Automatic Control and Computer sciences, Allerton Press, Inc., division of Pl. Publ. 2008, ? 6, pp 288-294
Abdullayeva, G.G., Ali-zadeh, C.A. & Hajiyev, Z.A. (2004). Intelligent system of optimization of choice of sort of operating interference. USA, CA: SPIE, Medical Imaging. URL: http://www.spie.org/vol.5371.
Alizade, U.M. (2018). Software package for recognition and identification of closed circuits in complex images (on the example of ultrasound images). The scientific heritage, ? 20, Vol. 1, 39-42.
Buy, T.T.C. & Spitsyn V.G. (2010). Analysis of edge detection methods for digital images. Proceedings of TUSUR, N.º 2 (22), Part 2.pp 221-223 [in Russian].
Gonzalez, R. & Woods, R. (2012). Digital Image Processing (3rd Edition). M.:Technosfera, 1104 p.
Huseynov, A.Z. & Huseynov, T.A. (2016). Modern diagnosis of liver tumors. Electronic Journal of New Medical Technologies, No 4. 19 p. [in Russian].
Kazakevich, V.I., Mitina, L.A., Stepanov, S.O. & Vostrov, A.N. (2016). Ultrasound diagnosis of tumors of the main localizations: general principles and approaches. Analytical review, personal observations. Department of Ultrasound Diagnostics, P.A. Hertsen Moscow Oncology Research Center, Moscow. [in Russian].
Longacre, A. Jr., Hawley, T. & Pankow, M. (2016). System and method to manipulate an image. Patent number: 9477867.
Nikolsky, Y.Y., Chekhonatskaya, M.L., Zuyev, V.V. & Zakharova, N.B. (2016). The possibilities of the ultrasound method in the diagnosis of tumors of the renal parenchyma. Bulletin of Medical Internet Conferences, Vol. 6, Is. 2. [in Russian]. pp. 282-284
Ognev, I.V. & Sidorova, N.A. (2007). Image processing by mathematical morphology methods in an associative oscillatory environment. Tekhnicheskiye nauki. Informatika i vychislitel’naya tekhnika, No 4, 87-97. [in Russian].
Shapiro, L. & Stockman, G. (2001). Computer Vision. Pearson.
Sinyukova, G.T., Gudilina, Y.A., Danzanova, T.Y., Sholokhov, V.N., Lepedatu, P.I., Allakhverdiyeva, G.F. & Kostyakova, L.A., Berdnikov S.N. (2016). Modern technologies of ultrasound imaging in the diagnosis of local recurrence of thyroid cancer. Medical Sciences, No 9(51). [in Russian]. pp. 81-84
Skouroliakou, C., Lyra, M., Antoniou, A. & Vlahos, L. (2006). Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research A, v.569, 606-609.
Sukhareva, Y.A. & Ponomareva, L.A. (2013). Characteristics of diseases of the mammary glands in adolescent girls visiting the mammology clinic. Tumors of the female reproductive system, (1-2), 40-4. [in Russian].
Zoph, B., Vasudevan, V., Shlens, J. & Le, V.Q. (2018). Learning Transferable Architectures for Scalable Image Recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8697-8710.
Abdullayeva, G.G. & Kazim-Zada, A.K. Recognition and identification of Plane Color Images in the Case of Carpet Designs. Automatic Control and Computer sciences, Allerton Press, Inc., division of Pl. Publ. 2008, ? 6, pp 288-294
Abdullayeva, G.G., Ali-zadeh, C.A. & Hajiyev, Z.A. (2004). Intelligent system of optimization of choice of sort of operating interference. USA, CA: SPIE, Medical Imaging. URL: http://www.spie.org/vol.5371.
Alizade, U.M. (2018). Software package for recognition and identification of closed circuits in complex images (on the example of ultrasound images). The scientific heritage, ? 20, Vol. 1, 39-42.
Buy, T.T.C. & Spitsyn V.G. (2010). Analysis of edge detection methods for digital images. Proceedings of TUSUR, N.º 2 (22), Part 2.pp 221-223 [in Russian].
Gonzalez, R. & Woods, R. (2012). Digital Image Processing (3rd Edition). M.:Technosfera, 1104 p.
Huseynov, A.Z. & Huseynov, T.A. (2016). Modern diagnosis of liver tumors. Electronic Journal of New Medical Technologies, No 4. 19 p. [in Russian].
Kazakevich, V.I., Mitina, L.A., Stepanov, S.O. & Vostrov, A.N. (2016). Ultrasound diagnosis of tumors of the main localizations: general principles and approaches. Analytical review, personal observations. Department of Ultrasound Diagnostics, P.A. Hertsen Moscow Oncology Research Center, Moscow. [in Russian].
Longacre, A. Jr., Hawley, T. & Pankow, M. (2016). System and method to manipulate an image. Patent number: 9477867.
Nikolsky, Y.Y., Chekhonatskaya, M.L., Zuyev, V.V. & Zakharova, N.B. (2016). The possibilities of the ultrasound method in the diagnosis of tumors of the renal parenchyma. Bulletin of Medical Internet Conferences, Vol. 6, Is. 2. [in Russian]. pp. 282-284
Ognev, I.V. & Sidorova, N.A. (2007). Image processing by mathematical morphology methods in an associative oscillatory environment. Tekhnicheskiye nauki. Informatika i vychislitel’naya tekhnika, No 4, 87-97. [in Russian].
Shapiro, L. & Stockman, G. (2001). Computer Vision. Pearson.
Sinyukova, G.T., Gudilina, Y.A., Danzanova, T.Y., Sholokhov, V.N., Lepedatu, P.I., Allakhverdiyeva, G.F. & Kostyakova, L.A., Berdnikov S.N. (2016). Modern technologies of ultrasound imaging in the diagnosis of local recurrence of thyroid cancer. Medical Sciences, No 9(51). [in Russian]. pp. 81-84
Skouroliakou, C., Lyra, M., Antoniou, A. & Vlahos, L. (2006). Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research A, v.569, 606-609.
Sukhareva, Y.A. & Ponomareva, L.A. (2013). Characteristics of diseases of the mammary glands in adolescent girls visiting the mammology clinic. Tumors of the female reproductive system, (1-2), 40-4. [in Russian].
Zoph, B., Vasudevan, V., Shlens, J. & Le, V.Q. (2018). Learning Transferable Architectures for Scalable Image Recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8697-8710.
Abdullayeva, G., & Alizade, U. (2019). An Information Recognition System for Complex Images. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(3), 79–93. https://doi.org/10.14201/ADCAIJ2019837993
Downloads
Download data is not yet available.
+
−