Brain MRI Segmentation Using Fuzzy Clustering Algorithms

Year 2021, Volume: 5 Issue: 4, 112 - 118, 30.12.2021

Fatemeh Jamalinabijan Ayda Baheri Eslami Gulcihan Ozdemir

Abstract

On the authority of human realization, the process of partitioning an image into non-overlapping and meaningful parts is called image segmentation. One of the traditional and conventional implementations of image segmentation is Brain MRI segmentation. In most cases, the MRI segmentation procedures are based on clustering approaches and according to the literature studies FCM based algorithms are more noticeable among other methods. Due to some drawbacks of FCM algorithm, like its weak function in the presence of noise, random initial values and easily falling into local optimal solution research have been trying to make some improvements on FCM algorithm. There are plenty of novel FCM based algorithms and In this work, we have implemented two FCM based algorithms (ARKFCM, SFCM2D) with different types of brain MRI images and compared them with conventional FCM to see which one has the better performance on the images with and without noise. Results are shown in the form of segmented images, and they demonstrate that ARK-FCM shows a better performance in keeping the details and being more resistant in working on noisy MRI images.

Keywords

Fuzzy, Image segmentation, clustering, Medical images

References

• [1] Hamza Abdellahoum , Nassim Mokhtari, Abderrahmane Brahimi, Abdelmadjid Boukra(2020). CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach.https://doi.org/10.1016/j.eswa.2020.114063
• [2] Yong Yang, Shuying Huang(2007).IMAGE SEGMENTATION BY FUZZY C-MEANS CLUSTERING ALGORITHM WITH A NOVEL PENALTY TERM. Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term. | Request PDF (researchgate.net)
• [3] Samina Naz, Hammad Majeed, Humayun Irshad(2010). Image Segmentation using Fuzzy Clustering: A Survey. DOI: 10.1109/ICET.2010.5638492
• [4] Baxtyar Ahmed, Muzhir Al-Ani(2020). Digital Medical Image Segmentation Using Fuzzy C-Means Clustering . DOI: 10.21928/uhdjst.v4n1y2020.pp51-58
• [5] Ahmed Elazab, Changmiao Wang, Fucang Jia, Jianhuang Wu, Guanglin Li, and Qingmao Hu(2015). Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy 𝐶-Means Clustering.http://dx.doi.org/10.1155/2015/485495
• [6] Jianhong Cai(2019). Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images.https://doi.org/10.1007/s10916-019-1459-2
• [7] Bing Nan Li, Chee Kong Chui, Stephen Chang, S.H. Ong(2011). Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. https://doi.org/10.1016/j.compbiomed.2010.10.007
• [8] Nameirakpam Dhanachandra , Yambem Jina Chanu(2020). An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm.https://doi.org/10.1007/s11042-020-08699-8
• [9] Rehna Kalam , Dr Ciza Thomas,Dr M Abdul Rahiman(2016).GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATION.DOI:10.5121/csit.2016.60405
• [10] R.Suganya, R.Shanthi(2012). Fuzzy C- Means Algorithm- A Review. http://www.ijsrp.org/research-paper-1112/ijsrp-p1168.pdf
• [11] Prabhjot Kaur, Tamalika Chaira(2020). A novel fuzzy approach for segmenting medical images.https://doi.org/10.1007/s00500-020-05386-6
• [12] C. Narmatha, Sarah Mustafa Eljack, Afaf Abdul Rahman Mohammed Tuka, S. Manimurugan, Mohammed Mustafa(2020). A hybrid fuzzy brain storm optimization algorithm for the classifcation of brain tumor MRI images. https://doi.org/10.1007/s12652-020-02470-5