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Saxena, C. L.
- A New Method for Texture Features Extraction (TFE) of Ultra Sound Image Based On Gabor Filter Segmentation Using Matlab
Authors
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
Source
Programmable Device Circuits and Systems, Vol 2, No 2 (2010), Pagination: 1-13Abstract
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.
- Result Analysis to Compute the Entropy of Voice Signal and SNR Using MatLab
Authors
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
Source
Biometrics and Bioinformatics, Vol 2, No 2 (2010), Pagination: 1-12Abstract
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.