On the type-1 and type-2 fuzziness measures for thresholding MRI brain images

Rajesh, R.; Senthilkumaran, N.; Satheeshkumar, J.; Priya, B. Shanmuga; Thilagavathy, C.; Priya, Kannappan

doi:10.1109/fuzzy.2011.6007444

Cited by 3 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [31], an evolving fuzzy classifier approach that is able to adapt and evolve at an on-line machine vision system is introduced. In [32], new modified thresholding measures for MRI brain images using type 1 and type 2 fuzzy sets are presented. An interval type 2 (IT2) fuzzy entropy based approach is used to compute optimum thresholds for multistage gray scale image segmentation in [33].…”

Section: Related Workmentioning

confidence: 99%

Color Image Segmentation Using Fuzzy C-Regression Model

Chen

Ludwig

2017

Advances in Fuzzy Systems

View full text Add to dashboard Cite

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%