Segmentation of images holds an important position in the area of image processing. It
becomes more important while typically dealing with medical images where pre-surgery and …

Image segmentation is an important prerequisite step for any automatic clinical analysis
technique. It assists in visualization of human tissues, as accurate delineation of medical …

Brain tumor diagnosis is a very crucial task. Magnetic resonance imaging (MRI) scan can be
used to produce image of any part of the body and it provides an efficient and fast way for …

Clustering of data is a method by which large sets of data are grouped into clusters of
smaller sets of similar data. Fuzzy c-means (FCM) clustering algorithm is one of the most …

The segmentation of images into different meaningful classes is an important task for
automatic image analysis technique. The finite Gaussian mixture model is one of the popular …

Image segmentation is a vital step in image processing and has attracted many researchers
towards its potential applications like object recognition, pattern recognition, computer vision …

The image segmentation is performed to detect, extract and characterize the anatomical
structure. Here, we apply two widely used algorithm for tumour detection (i) K-means …

The finite Gaussian mixture model is one of the most popular frameworks to model classes
for probabilistic model-based image segmentation. However, the tails of the Gaussian …

In this paper color based image segmentation is done in two spaces. First in LAB color
space and second in RGB space all that done using three versions of K-Means: K-Means …

Spectro-temporal representation of speech has become one of the leading signal
representation approaches in speech recognition systems in recent years. This …