Adaptive Region Growing Image Segmentation Algorithms for Breast MRI
Joe Arun Raja1, Nelson Kennedy Babu2

1Joe Arun Raja, Department of Networking, Subbalakshmi Lakshmipathy College of Science, Madurai & Research Scholar, Manonmaniam Sundaranar University. Tirunelveli-627012.
2Nelson Kennedy Babu, Department of CSE , Saveetha University, Chennai-600077, India. 

Manuscript received on 10 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 8729-8732 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5912098319/2019©BEIESP | DOI: 10.35940/ijrte.C5912.098319

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Abstract: Early detection and characterization of breast lesion are important for a better and effective treatment of breast cancer. In this paper, four different adaptive region growing image segmentation algorithms are compared. In fact, seed selection was a vital step in the success of region growing methods, so, better schemes for seed selection methods are proposed, namely, joint probabilistic seed selection (JPSS) and Generalised simulated annealing (GSA) based seed selection. The proposed region growing methods namely Fuzzy Region Growing (FRG) and Neutrosophic Region Growing (NRG) are integrated as JPSS-FRG and GSA-NRG frameworks. Another two methods are Scale Invariant Region growing (SiRG) and Fuzzy Neutrosophic Confidence Region growing (FNCRG). The results showed that FNCRG algorithm increases breast cancer detection rate on MRI breast images with the maximum of 93% is achieved. SiRG algorithm improves the true positive rate by 13% compared to existing methods. Further, GSA-NRG makes better segmentation accuracy by 9% and true positive rate by 12%. Also, JPSS-FRG algorithm enhances segmentation accuracy by 24% and improving the true positive rate by 27% compared to Region Growing-Cellular Neural Network (RG-CNN) and Seeded Region Growing-Particle swarm optimization (SRG-PSO) methods respectively.
Keywords: Breast MRI, Fuzzy Logic, Neutrosophic Logic, Region Growing Algorithm.

Scope of the Arti Gcle: Fuzzy Logic