Abstract The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM ensemble approach that takes local information into account (LI_BIFCM) overcome these challenges increase quality. First, various membership matrices are created after running multiple times, based on the randomization i...

Magnetic Resonance Imaging (MRI) offers a wealth of information for medical examination. Fast, accurate and reproducible segmentation of MRI is desirable in many applications. We have developed a new unsupervised MRI segmentation method based on k-means and fuzzy c-means (FCM) algorithms, which uses spatial constraints. Spatial constraints are included by the use of a Markov Random Field model....

There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess development of vehicular specifications this context to evaluate changes powertrain technologies depending on surrounding frame conditions, such as charging stations vehicle taxation policy, we need a detailed understanding fleet composition. This paper aims therefore introduce novel ...

The clustering algorithm hybridization scheme has become of research interest in data partitioning applications in recent years. The present paper proposes a Hybrid Fuzzy clustering algorithm (combination of Fuzzy C-means with extension and Subtractive clustering algorithm) for data classifications applications. The fuzzy c-means (FCM) and subtractive clustering (SC) algorithm has been widely d...

A 3D rib cage model helps to study anatomical structures in some medical applications such as biomechanical and surgical operations. Its quality directly depends on rib cage segmentation if it is reconstructed from image data. This paper presents an optional segmentation method based on K-means clustering. It uses a hierarchical concept to control the clustering, and it organizes clustered regi...

Information granules have been considered as the fundamental constructs of Granular Computing. As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one most frequently used methods to construct information granules. The FCM-based granulation–degranulation mechanism plays pivotal role in In this paper, enhance quality degranulation (reconstruction) process, we augment by introduci...

Synthetic aperture side-scan sonar (SAS) provides an imaging modality for detecting objects on the sea floor. It is also an excellent tool for shallow water characterization where immobile, submerged threats would not be detected by conventional forward-looking sonar range-doppler techniques. SAS images provide an image of an object and its shadow, both of which can be used in the classificatio...

In this paper we introduce Median Fuzzy C-Means (MFCM). This algorithm extends the Median C-Means (MCM) algorithm by allowing fuzzy values for the cluster assignments. To evaluate the performance of M-FCM, we compare the results with the clustering obtained by employing MCM and Median Neural Gas (MNG).

In this paper, the performance of the various fuzzy based algorithms for medical image segmentation is presented. Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, different types of fuzzy algorithms are introd...

This paper presents an approach to medical image registration using a segmentation step segmentation based on Fuzzy C-Means (FCM) clustering and the Scale Invariant Feature Transform (SIFT) for matching keypoints in segmented regions. To obtain robust segmentation, FCM is applied on feature vectors composed by local information invariant to image scaling and rotation, and to change in illuminat...