Abstract
Segmentation is an essential task in image analysis process. Due to non-homogeneous intensities, blurred boundaries, noise and minimum of contrast it is a challenging task for image analysists. It has wide range of applications in all fields exclusively in the field medical imaging for disease deionization and early detection. The root cause for non-homogeneous intensities is uncertainty. Various tools have been introduced to handle uncertainty. We have introduced type-2 fuzzy based image segmentation process for edge detection in blurred areas of an image. When compared with classical fuzzy set, it has upper and lower membership values. Since it has more membership values it can handle higher level of uncertainty. In this chapter we have proposed equivalence function associated with strong negation relation which will address each intensity \({\mathcal{I}}_{t}\) of an image \(\mathcal{I}\) through the membership values. This method is verified with thermographic breast cancer image data set and the results were satisfactory.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Benchara, F.Z., Youssfi, M.: A new distributed type-2 fuzzy logic method for efficient data science models of medical informatics. Adv. Fuzzy Syst. (2020). https://doi.org/10.1155/2020/6539123
Bottema, M.J., Slavotinek, J.P.: Detection and classification of lobular and DCIs microcalcifications in digital mammograms. Pattern Recogn. Lett. 24, 1209–1214 (2000)
Castillo, O., Sanchez, M.A., Gonzalez, C.I., Martinez, G.E.: Review of recent Type-2 fuzzy image processing applications. Information 8(97), 1–18 (2017)
Castillo, O., Melin, P., Castro, J.R.: Computational Intelligence Software for Interval Type-2 Fuzzy Logic. Proceedings of the 2008 Workshop on Building Computational Intelligence and Machine Learning Virtual Organizations, pp. 9–29 (2008)
Chouhan, S.S., Kaul, A., Singh, U.P.: Soft computing approaches for image segmentation-a survey. Multimed Tools Appl. 77, 28483–28537 (2018)
Dawoud, A.: Segmentation of Dermoscopic images by the fusion of Type-2 fuzziness measure in graph cuts image Binarization. Int. J. Imaging Robot 2(15), 73–87 (2015)
Devi, M., Singh, S., Tiwari, S., Patel, S.C., Ayana, M.T.: A survey of soft computing approaches in biomedical imaging. Hindawi J. Healthcare Eng. 21, 2040–2295 (2021)
Ensafi, P., H.R., Tizhoosh, R.: Type-2 Fuzzy Image Enhancement. Springer, Berlin, Heidelberg, LNCS 3656, pp. 159–166 (2005)
Gonzalez, C.I., Melin, P., Castillo, O.: Edge detection method based on general Type-2 fuzzy logic applied to colour images. Information 8(104), https://doi.org/10.3390/info8030104 (2017)
Hagras, H.A.: A Hierarchical Type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12, 524–539 (2004)
Idris, N.F., Ismail, M.A.: Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition. Peer J. Comput. Sci., e427 (2021). https://doi.org/10.7717/peerj-cs.427
Jafar, T., Chunwei, Z., Ardashir, M.Z., Saleh, M., Mosavi Amir, H.: Medical image interpolation using recurrent Type-2 Fuzzy neural network. Front. Neuro-Inf. 15(75) (2021)
Krinidis, S., Chatzis, V.: Fuzzy Energy-based active contours. Trans. Image Process. 18, 2747–2755 (2009)
Mendel, J.M.: Uncertainty, fuzzy logic, and signal processing. Signal Process 80, 913–933 (2000)
Mendel, J.M.: Type-2 Fuzzy Sets and Systems—A Retrospective, pp. 523–532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/s00287-015-0927-4 (2015)
Mendel, J.M., Bob John, R.I.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)
Mijares, S.T., Woo, F., Flores, F.: Breast cancer identification via thermography image segmentation with a gradient vector flow and a convolutional neural network. J. Healthcare Eng. 98(19). https://doi.org/10.1155/2019/980619 (2019)
Murugeswari, P., Vijayalakshmi, S.: New method of Interval type-2 fuzzy-based CNN for image classification. Int. J. Fuzzy Logic Intell. Syst. 20, 336–345 (2020)
Nguyen, D.D., Ngo, L.T., Watada, J.: A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation. J. Intell. Fuzzy Syst. 27, 3111–3122 (2014)
Palanivel, M., Duraisamy, M.: Adaptive color texture image segmentation using α-cut implemented interval Type-2 Fuzzy C-Means. Res. J. Appl. Sci. 7, 258–265 (2012)
Palanivel, M., Duraisamy, M.: Color textured image segmentation using ICICM-Interval Type-2 Fuzzy C-Means clustering hybrid approach. Eng. J. 16(5), 115–126 (2012)
Pereira, C.L., Bastos, C.A.C.M.: Tsang Ing Ren & George D.C. Cavalcanti: fuzzy active contour models. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp. 1621–1627 (2011)
Shan, J., Cheng, H.D., Wang, Y.: Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med. Biol. 38(2), 262–275 (2012)
Shikkenawis, G., Mitra, S.K.: Image denoising using 2D orthogonal locality preserving discriminant projection. J. IET Image Process. 14(3), 554–560 (2019)
Silva, L.F., Saade, D.C.M., Sequeiros, G.O.: A new database for breast research with infrared image. J. Med. Imaging Health Inf. 4(1), 92–100 (2014)
Xian, M., Zhang, Y., Cheng, H.D.: Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recogn. 48(2), 485–497 (2015)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Anitha, K., Datta, D. (2023). Type-2 Fuzzy Set Approach to Image Analysis. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-031-26332-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-26332-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26331-6
Online ISBN: 978-3-031-26332-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)