In this paper we modify the existing image processing schema for computed tomography
perfusion (CTP) maps analysis in order to increase its efficiency in detection and …

In this paper we present a fully automatic and unsupervised brain tumor segmentation
method which considers human knowledge. The expert knowledge and the features derived …

The present chapter proposes an automatic segmentation method that performs multilevel
image thresholding by using the spatial information encoded in the gray level co-occurrence …

Multilevel thresholding is one of the most popular image segmentation techniques due to its
simplicity and accuracy. Most of the thresholding approaches use either the histogram of an …

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic
fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor …

Objectives: To segment tumor with higher accuracy. Methods/Statistical Analysis: Noise
removal is done with the help of Gabor filter as a preprocessing step. Skull stripping is done …

Although there are several data analysis frameworks, both commercial and open source,
supporting the detection of tumours on nuclear magnetic resonance (NMR) sequences …

This study uses machine learning techniques (ML) to classify and cluster different Western
music genres. Three artificial neural network models (multi-layer perceptron neural network …

Purpose: Glioblastoma is the most common malignant brain tumor. It is characterized by low
median survival time and high survival variability. Survival prognosis for glioblastoma is very …

This study uses self-organizing maps (SOM) to examine the effect of various psychographic
and cognitive factors on organ donation in Egypt. SOM is a machine learning method that …