Advances in Intelligent Informatics (Advances in Intelligent by Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn PDF

By Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne

This e-book encompasses a choice of refereed and revised papers of clever Informatics music initially awarded on the 3rd foreign Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this music conceal numerous clever informatics and similar themes together with sign processing, development acceptance, photograph processing, info mining and their functions.

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Extra info for Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320)

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The next step is to cluster the gray levels of both the images and map it to the reference image, . That is, a single intensity pixel of 8-bits is represented by a three layer pixel of 24-bits, reversed engineered from to . In such a way, the single dimension (1D) is transformed _ to a three dimension (3D) colored image, . This colored image can be used in-for segmentation or any other IP operations. Grayscale to Color Map Transformation for Efficient Image Analysis 13 The mapping of a single pixel for 8-bit grayscale image to 24-bit color image.

The Neural network used in this work is a feed forward neural network. The training algorithm considered here is a back propagation method. The number of nodes in the input layer is thirty five which corresponds to the total number of features extracted. The number of nodes in the hidden layer is kept as twenty five and fifty and the performance of the classifier is evaluated. The output layer contains two nodes corresponding to the two classes namely ‘tumor affected’ or ‘tumor not affected’. 2 Support Vector Machine Classifier Support vector machine is a learning method used for classification.

Brain Tumor classification using Discrete Cosine transform and Probabilistic neural network. In: International Conference on Signal Processing Image Processing & Pattern Recognition (ICSIPR), pp. : Brain tumor MRI image classification with feature selection and extraction using Linear Discriminant analysis. : An efficient approach for brain tumor detection based on modified region growing and Neural Network in MRI images. In: International Conference on Computing, Electronics, Electrical Technologies, pp.

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