Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors

Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI)

“…), the breadth and depth of the Gaussian membership function can be considered. This helps the user to include all the pixels for segmentation without any omission or negligence of pixel values (motivated by Qiu et al [26], Vishnuvarthanan et al [2], and Alagarsamy et al [27]). The scalar unit used for membership formulation is named for convenience as AV parameter, and this parameter is used to define lower membership and higher membership functions of the IT2FLS algorithm.…”

“…The above sequential steps show the combination of SOM and IT2FLS techniques, which has been developed with an objective to render efficient MR brain slice segmentation, and most of the process steps used here have been referred from the research works carried by Anitha et al [3] and Alagarsamy et al [27].…”

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

“…The validation of this algorithm in (J. L. Jiang et al, 2019 ) was performed on a Hear disease (Statlog) and Breast cancer dataset. Besides, many other applications made use of BA, such as MR brain image segmentation ( Alagarsamy, Kamatchi, Govindaraj, Zhang, & Thiyagarajan, 2019 ), human diseases prediction ( Enireddy et al, 2021 ), and pathological brain detection ( Lu, Wang, & Zhang, 2020 ).…”

Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

“…On the other side, Rigon Based classified the image into sub regions (clusters) (Zaitoun & Aqel, 2015). There are five categories of Rigon Based segmentation method; split and merge (Ning et al, 2010), normalized cuts (Shi & Malik, 2000), region growing (Tang, 2010), threshold (Mishra & Panda, 2018;Sarkar et al, 2011), and finally clustering (Alagarsamy et al, 2019;Zhang et al, 2011). This study is focused on the region based color image segmentation specified in clustering method.…”

“…Clustering can be defined as a process of splitting homogeneous objects in one cluster and heterogeneous objects in another cluster (Kapoor et al, 2017). In literature, there are different methods of clustering based such as: k-means clustering (Dhanachandra et al, 2015;Mathur & Purohit, 2014), DBScan clustering method (Ye et al, 2003), subtractive clustering (Dhanachandra et al, 2015), and most recently used intelligent inspired optimization algorithms (i.e.meta-heuristic algorithms) in clustering like Wolf optimization algorithm (Kapoor et al, 2017), Cuckoo search algorithm (Nandy et al, 2015), Particle Swarm Optimization (Zhang et al, 2011), Bat optimization algorithm (Alagarsamy et al, 2019;Mishra & Panda, 2018;Yang, 2010) and most recently introduced Whale Optimization algorithm (Mirjalili & Lewis, 2016).…”