Informatics Publishing Limited

Shiv Kumar ¹, R. K. Singh ², Vijay K. Chaudhari ¹, Sushil Kumar ³, C. L. Saxena ⁴

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 MATS University, Raipur (C.G.), IN
3 Department of Information Technology, Technocrat Institute of Technology & Exellence -Bhopal (M.P.), IN
4 Technocrat Institute of Technology-Bhopal (M.P.), IN

Abstract

In this paper titled: “A New Method For Texture Features Extraction (TFE) Of Ultra Sound Images Based OnGabber Filter Segmentation Using Matlab” is used for to detect abnormal structural changes of ultra sound images. In research is being conducted with the objective to innovatively develop and apply image segmentation and image feature extraction techniques to efficiently segment the ultrasound image which can be used to automatically detect the diseases in different organs of the body. The process of identifying regions with similar texture and separating regions with different texture is used for the segmentation of ultrasound images.Firstly, a multi-channel texture analysis technique that relies on 2D Gabor Filters is used to isolate regions of perceptually homogeneous texture in an image. Textures are modeled as patterns dominated by a narrow band of spatial frequencies and orientation. Properly tuned Gabor filters react strongly to specific textures and weakly to all others. Then the features of the image were extracted from the image and the clustering of pixels in the feature space produced the segmented image. Unsupervised approach is used for texture segmentation. K-mean clustering method is proposed to cluster the pixels belonging to the same texture region provided the number of different textures in the image is known beforehand.

Keywords

MatLab7.0, Image Processing Toolbox, Biometrics Toolbox, Gabor Filter, Gauss Filter, Clustering, Algorithms.

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Shiv Kumar ¹, R. K. Singh ², Manish Srivastava ³, Dinesh Varshney ⁴, Vijay K. Chaudhari ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 MATS University, Raipur (C.G.), IN
3 Department of Computer Science and Engineering, Guru Ghansidas Central University, Bilaspur (C.G.), IN
4 Multimedia Regional Centre, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN

Abstract

In this paper titled "A New Approach for Tree-Structured Wavelet Transform based Texture Retrieval Analysis (TRA) by Using MatLab" is define Wavelet transform-based texture analysis, as I found in the different research, uses sub-band energy values as features, but not the order of energy values. In fact, a textured image, after a wavelet decomposition, yields an energy distribution which can be rank ordered with respect to the sub-bands. It has been found that the combination of the sub-band energy value and its ranking order leads to a more efficient texture retrieval mechanism.

Keywords

MatLab7.0, Wavelet Toolbox, Image Processing Toolbox, Algorithm.

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Shiv Kumar ¹, Vijay K. Chaudhari ², R. K. Singh ³, Dinesh Varshney ⁴, C. L. Saxena ⁵

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
3 MATS University, Raipur (C.G.), IN
4 Multimedia Research Department, Multimedia Regional Center, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN
5 Technocrats Institute of Technology, Bhopal (M.P.), IN

Abstract

In this project report, (i.e. “Result Analysis to Compute Entropy of Voice Signal (CEVS) SNR using Matlab”) an approach is used to compute the entropy of given voice signal and signal to noise ratio (SNR) with the help of computed entropy. The main goals of this project are:
• To compute the tone of inputted voice signal
• To estimate entropy of tone
• To calculate SNR of entropy
To do this, psychoacoustic model and wavelet toolbox is used. Psychoacoustic model calculates masking threshold. Maximum distortion energy is computed from computed tone of inputted voice signal which defines the CEVS and SNR.

Keywords

Matlab 6.5, Wavelet Toolbox, Psychoacoustic Model, Algorithm.

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Shiv Kumar ¹, Vijay K. Chaudhari ², Md. Ilyas Khan ³, Neetesh Gupta ⁴, Bupendra Verma ⁵

Affiliations
1 Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology at Technocrat Institute of Technology-Bhopal (M.P.), IN
3 Technocrats Institute of Technology, Bhopal, IN
4 Department of CSE/IT at Technocrat Institute of Technology-Bhopal (M.P.), IN
5 Gandhi Technical University, Bhopal(M.P.), IN

Abstract

In this paper “Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering” is an approach which proposed dynamically Growing Hierarchical Self Organizing Map (DGHSOM) with Nano array to identify co-expressed genes. The DGHSOM overcomes the problem of specifying the number of clusters and total number of iteration before the processing now, we are using QT (quality threshold) clustering is a method of partitioning data, which is invented for gene clustering. It requires more computing power than k- means, but does not require specifying the number of clusters. DNA Nano array technology is a challenging area in bioinformatics research, as we have to monitor millions of genes simultaneously. The expression profile of the gene can be useful in cancer disease analysis and its diagnosis. Gene expression data is very voluminous and very difficult to analyze. Several clustering algorithm have been proposed to identify co expressed genes. The Self-organizing-maps (SOM) is a powerful tool for recognizing and classifying features in complex, micro array data. But the interpretation of co- expression of genes are heavily depends on domain knowledge and SOM lacks since the number of clusters must be determined before training.

Keywords

Gene Expression Profile, Image Processing, Dynamically Growing Self Organizing Map, Nano Array, Qt Clustering.

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