Enhancing Segmentation Approaches from GC-OAAM and MTANN to FUZZY K-C-MEANS

Medical Image Segmentation is an activity with huge handiness. Biomedical and anatomical data are
made simple to acquire because of progress accomplished in computerizing picture division. More
research and work on it has improved more viability to the extent the subject is concerned. A few techniques
are utilized for therapeutic picture division, for example, Clustering strategies, Thresholding
technique, Classifier, Region Growing, Deformable Model, Markov Random Model and so forth. This
work has for the most part centered consideration around Clustering techniques, particularly k-implies
what's more, fluffy c-implies grouping calculations. These calculations were joined together to
concoct another technique called fluffy k-c-implies bunching calculation, which has a superior outcome
as far as time usage. The calculations have been actualized and tried with Magnetic Resonance
Image (MRI) pictures of Human cerebrum. The proposed strategy has expanded effectiveness and
lessened emphasis when contrasted with different techniques. The nature of picture is assessed by figuring
the proficiency as far as number of rounds and the time which the picture takes to make one
emphasis. Results have been dissected and recorded. Some different strategies were surveyed and
favorable circumstances and hindrances have been expressed as special to each. Terms which need to
do with picture division have been characterized nearby with other grouping strategies.
Keywords: Graph Cut Method, Active Contours Model, Geodesic Graph Cut Method, Graph-Cut
Oriented Active Appearance Model (GC-OAAM), Massive Training Artificial Neural Network
(MTANN), Fuzzy-K-C-Means Segmentation Method.