Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad; Mustafa, Iskandar Shahrim; Abood, Loay Kadom

doi:10.1063/1.4915191

Cited by 4 publications

(1 citation statement)

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“…Because everything is correlated, the similar things are more closely related, but the fuzzy theory does not consider the correlation of image neighborhood, and the Markov Random Field (MRF) theory points out that under the condition that the state of any pixel is known, the probability of the state of random field at the pixel is related to the state of its neighborhood, which can effectively divide the texture and edge of the image. Therefore, the new algorithm combines FCM and MRF together to computed image segmentation [17][18][19][20][21][22][23]. In addition, Zhou [24] developed a novel classification optimization approach integrating class adaptive MRF and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI).…”

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Obtaining Land Cover Type for Urban Storm Flood Model in UAV Images Using MRF and MKFCM Clustering Techniques

Wang

Zhang

et al. 2019

IJGI

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With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic.

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confidence: 99%