Intelligent Approaches towards Fuzzy Segmentation and Fuzzy Edge Detection
Fuzzy method is one of the most popular methods for image segmentation. In this paper, fuzzy segmentation and fuzzy edge detection methods are presented to segment and detect the edges of the images. The experimental results of our proposed method show that this algorithm performs well and it segments and detects the edges of the image precisely.
Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in spa hotels through tripadvisors online reviews. International Journal of Hospitality Management, 80, 52-77.
Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A.R., Knox, K., Samad, S. and Ibrahim, O., (2019). Revealing customers satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, pp.331-343.
Ahmadi, N. (2019). Morphological-Edge Detection Approach for the Human Iris Segmentation. Journal of Soft Computing and Decision Support Systems, 6(4), 15-19.
Ahmadi, N., & Akbarizadeh, G. (2015). Iris recognition system based on canny and LoG edge detection methods. Journal of Soft Computing and Decision Support Systems, 2(4), 26-30.
Ahmadi, N., & Akbarizadeh, G. (2016). A review of iris recognition based on biometric technologies. Transylvanian Rev, 24(4), 151-163.
Ahmadi, N., & Akbarizadeh, G. (2017). Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO. Iet Biometrics, 7(2), 153-162.
Ahmadi, N., & Akbarizadeh, G. (2018). Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Computing and Applications, 1-15.
Ahmadi, N., & Nilashi, M. (2018). Iris texture recognition based on multilevel 2-D Haar wavelet decomposition and Hamming distance approach. Journal of Soft Computing and Decision Support Systems, 5(3), 16-20.
Ahmadi, N., Nilashi, M., Samad, S., Rashid, T. A., & Ahmadi, H. (2019). An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120, 105701.
etin, M., Dokur, Z., & lmez, T. (2019, April). Fuzzy Local Information C-Means Algorithm for Histopathological Image Segmentation. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) (pp. 1-6). IEEE.
Chen, X., & Pan, L. (2018). A survey of graph cuts/graph search based medical image segmentation. IEEE reviews in biomedical engineering, 11, 112-124.
Chouhan, S. S., Kaul, A., & Singh, U. P. (2018). Soft computing approaches for image segmentation: a survey. Multimedia Tools and Applications, 77(21), 28483-28537.
Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., ... & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical image analysis, 44, 1-13.
Edalati-rad, A., & Mosleh, M. (2019). Improving Brain Tumor Diagnosis Using MRI Segmentation Based on Collaboration of Beta Mixture Model and Learning Automata. Arabian Journal for Science and Engineering, 44(4), 2945-2957.
Gmez, D., Yez, J., Guada, C., Rodrguez, J. T., Montero, J., & Zarrazola, E. (2015). Fuzzy image segmentation based upon hierarchical clustering. Knowledge-Based Systems, 87, 26-37.
Hooda, H., Verma, O. P., & Arora, S. (2019). Optimal Fuzzy C-Means Algorithm for Brain Image Segmentation. In Applications of Artificial Intelligence Techniques in Engineering (pp. 591-602). Springer, Singapore.
Huang, H., Meng, F., Zhou, S., Jiang, F., & Manogaran, G. (2019). Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access, 7, 12386-12396.
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2018). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 1-13.
Mardani, A., Hooker, R. E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H. Z., & Fei, G. C. (2019). Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: a review of three decades of research with recent developments. Expert Systems with Applications, 137, 202-231.
Naidu, M. S. R., Kumar, P. R., & Chiranjeevi, K. (2018). Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria engineering journal, 57(3), 1643-1655.
Nilashi, M., Ahani, A., Esfahani, M.D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., Sharef, N.M. and Akbari, E., (2019a). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, pp.767-783.
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019e). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.
Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCAANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.
Nilashi, M., Ibrahim, O., & Ahani, A. (2016). Accuracy improvement for predicting Parkinsons disease progression. Scientific reports, 6, 34181.
Nilashi, M., Ibrahim, O., Samad, S., Ahmadi, H., Shahmoradi, L., & Akbari, E. (2019c). An analytical method for measuring the Parkinsons disease progression: A case on a Parkinsons telemonitoring dataset. Measurement, 136, 545-557.
Nilashi, M., Samad, S., Manaf, A.A., Ahmadi, H., Rashid, T.A., Munshi, A., Almukadi, W., Ibrahim, O. and Ahmed, O.H., (2019b). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, p.106005.
Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., & Sanzogni, L. (2019d). Analysis of Travellers Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal of Fuzzy Systems, 21(5), 1367-1378.
Panigrahi, L., Verma, K., & Singh, B. K. (2019). Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution. Expert Systems with Applications, 115, 486-498.
Sarkar, J. P., Saha, I., & Maulik, U. (2016). Rough Possibilistic Type-2 Fuzzy C-Means clustering for MR brain image segmentation. Applied Soft Computing, 46, 527-536.
Soualmi, A., Alti, A., & Laouamer, L. (2018). A new blind medical image watermarking based on weber descriptors and Arnold chaotic map. Arabian Journal for Science and Engineering, 43(12), 7893-7905.
Subashini, M. M., Sahoo, S. K., Sunil, V., & Easwaran, S. (2016). A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Systems with Applications, 43, 186-196.
Viswanathan, P. (2015). Fuzzy C means detection of leukemia based on morphological contour segmentation. Procedia Computer Science, 58, 84-90.
Wang, L., Bi, S., Lu, X., Gu, Y., & Zhai, C. (2019). Deformation measurement of high-speed rotating drone blades based on digital image correlation combined with ring projection transform and orientation codes. Measurement, 148, 106899.
Wang, W., Song, W., Wang, G., Zeng, G., & Tian, F. (2019). Image recovery and recognition: a combining method of matrix norm regularisation. IET Image Processing.
Winston, J. J., & Hemanth, D. J. (2018). A comprehensive review on iris image-based biometric system. Soft Computing, 1-24.
Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018). Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 66, 364-386.
Yin, S., Zhang, Y., & Karim, S. (2018). Large scale remote sensing image segmentation based on fuzzy region competition and Gaussian mixture model. IEEE Access, 6, 26069-26080.
Zhi, X. H., & Shen, H. B. (2018). Saliency driven region-edge-based top down level set evolution reveals the asynchronous focus in image segmentation. Pattern Recognition, 80, 241-255.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.