English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Nuclear image has emerged as a promising research work in medical field.Images from different modality meet its own challenge. Positron EmissionTomography (PET) image may help to precisely localize disease to assist inplanning the right treatment for each case and saving valuable time. In thispaper, a novel approach of Spatial Fuzzy C Means (PET SFCM) clusteringalgorithm is introduced on PET scan image datasets. The proposed algorithm isincorporated the spatial neighborhood information with traditional FCM andupdating the objective function of each cluster. This algorithm is implementedand tested on huge data collection of patients with brain neuro degenerativedisorder such as Alzheimers disease. It has demonstrated its effectiveness bytesting it for real world patient data sets. Experimental results are comparedwith conventional FCM and K Means clustering algorithm. The performance of thePET SFCM provides satisfactory results compared with other two algorithms

Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder