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

This booklet includes a collection of refereed and revised papers of clever Informatics song initially provided on the 3rd overseas Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this music conceal a number of clever informatics and similar themes together with sign processing, trend acceptance, picture processing, facts mining and their purposes.

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

This booklet includes a collection of refereed and revised papers of clever Informatics song initially provided on the 3rd overseas Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this music conceal a number of clever informatics and similar themes together with sign processing, trend acceptance, picture processing, facts mining and their purposes.

Show description

Read or Download Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320) PDF

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

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This involves a FCM and an ACM algorithm. 1 23 Fuzzy C Means Algorithm FCM is an unsupervised clustering algorithm which partitions the image into desired number of regions. It reduces the burden of providing ground truth data sets. The input to this algorithm consists of dataset and the required number of clusters. In this work the numbers of clusters are four which represents the gray matter, white matter, cerebrospinal fluid and the high intensity cluster. FCM works by assigning membership values to the pixels and assigns it to one of the cluster based on membership value.

P. Sinha 4. : Combining top-down and bottom-up segmentation. In: Workshop. CVPR, pp. 46–53 (2004) 5. : Detection of disease using block-based unsupervised natural plant leaf color image segmentation. C. ) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 399–406. Springer, Heidelberg (2011) 6. : Unsupervised resolution independent based natural plant leaf disease segmentation approach for mobile devices. In: Proc. id=2528240&preflayout=tabs (Cited April 10, 2014) 7. : The decolorize algorithm for contrast enhancing, color to grayscale conversion.

This work has developed a CAD system for automatically classifying the given brain Magnetic Resonance Imaging (MRI) image into ‘tumor affected’ or ‘tumor not affected’. The input image is preprocessed using wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE). The image is then quantized and aggregated to get a reduced image data. The reduced image is then segmented into four regions such as gray matter, white matter, cerebrospinal fluid and high intensity tumor cluster using Fuzzy C Means (FCM) algorithm.

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