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Journal of Cognitive Human-Computer Interaction
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Title

An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor

  P. Kavitha 1 * ,   R. Subha Shini 2 ,   R. Priya 3

1  Department of Computer Science and Engineering, Panimalar Engineering College, India
    (varshnikavitha@gmail.com)

2  Department of Computer Science and Engineering, Panimalar Engineering College, India
    (subha.rickz.007@gmail.com)

3  Department of Computer Science and Engineering, Panimalar Engineering College, India
    (priyasrp@gmail.com)

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Doi   :   DOI: https://doi.org/10.54216/JCHCI.010202

Received: June 05, 2021 Accepted: December 05, 2021

Abstract :

A member of a population who is at risk of becoming infected by disease is a susceptible individual. Finding disease susceptibility and generating an alert in advance, is valuable for an individual. The aim of the work presented a feature vector using different statistical texture analyses of brain tumors from an MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of brain tumor cell structure. For this paper, the brain tumor cell segmented using the strip method to implement hybrid Assured Convergence Particle Swarm Optimization (ACPSO) - Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o, and 135o have calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed on different types of images using past years. So, the algorithm proposed statistical texture features are calculated for iterative image segmentation. The algorithm FETC (Feature Extraction Tumor Cell) extracts statistical features of GLCM. These results show that MRI images can be implemented in a system of brain cancer detection. 

Keywords :

PSO , ACPSO , GLCM , FCM

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Cite this Article as :
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MLA P. Kavitha, R. Subha Shini, R. Priya. "An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor." Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 2, 2021 ,PP. 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
APA P. Kavitha, R. Subha Shini, R. Priya. (2021). An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor. Journal of Journal of Cognitive Human-Computer Interaction, 1 ( 2 ), 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
Chicago P. Kavitha, R. Subha Shini, R. Priya. "An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor." Journal of Journal of Cognitive Human-Computer Interaction, 1 no. 2 (2021): 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
Harvard P. Kavitha, R. Subha Shini, R. Priya. (2021). An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor. Journal of Journal of Cognitive Human-Computer Interaction, 1 ( 2 ), 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
Vancouver P. Kavitha, R. Subha Shini, R. Priya. An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor. Journal of Journal of Cognitive Human-Computer Interaction, (2021); 1 ( 2 ): 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
IEEE P. Kavitha, R. Subha Shini, R. Priya, An Implementation Of Statistical Feature Algorithms For The Detection Of Brain Tumor, Journal of Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 2 , (2021) : 57 - 62 (Doi   :  DOI: https://doi.org/10.54216/JCHCI.010202)
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