Unification of firefly algorithm with density-based spatial clustering for segmentation of medical images

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This paper proposes a computer-aided approach for brain image segmentation to figure out various characteristics of digital images which are responsible for the identification of brain tumour with MRI images. The proposed Density-Based Spatial Clustering Fused with Firefly (DB-FF) method is based on Density-Based Spatial Clustering and Firefly Algorithm which has a significant place in nature-inspired computing techniques. In this research, the solutions of the firefly algorithm have been improved by the density-based spatial clustering algorithm and a soft computing criterion has also been used as a fitness function. The proposed method has been tested on commonly used images from Harvard Whole Brain Atlas and the results of this method have been compared with other standard benchmarks from the survey. The proposed DB-FF method achieved better segmentation than standard segmentation quality metrics such as normalised peak signal to noise, normalised root square mean error and structural similarity index metric. Matlab has been used for implementation and observation. The result demonstrates that the proposed method has a better and robust performance as compared with the existing MRI segmentation models.