Contrast Enhancement Based Brain Tumour MRI Image Segmentation And Detection With Low Power Consumption

Vol. 3  Issue 2
Year: 2016
Issue:Apr-Jun
Title:Contrast Enhancement Based Brain Tumour MRI Image Segmentation And Detection With Low Power Consumption
Author Name:B. Shoban Babu, S. Varadarajan and S. Swarnalatha
Synopsis:
Medical image processing is the most challenging topic in the research field. Brain tumour is a serious life altering disease condition. Image segmentation plays a significant role in the estimation of suspicious regions from the medical images. In this paper, two algorithms have been proposed; one algorithm for image enhancement and the other algorithm for image segmentation. The MRI image wherein the brain tumour is to be detected, is enhanced using the proposed technique called Power Constrained Contrast Enhancement (PCCE). Thus, the obtained enhanced image is subjected to image segmentation using threshold point method, to detect the brain tumour. The performance of the proposed method is analysed by computing the parameters of the brain tumour like volume, area, Contrast, Emissive display, mean and standard deviation. These parameter values are compared with the earlier methods, and it found that the performance of the proposed technique of brain tumour detection is quite satisfactory over the earlier existing techniques.

Comparative Analysis Of Edge Detection Techniques.

Vol. 3  Issue 2
Year: 2016
Issue:Apr-Jun
Title:Comparative Analysis Of Edge Detection Techniques.
Author Name:Navkamal Kaur and Beant Kaur
Synopsis:
This paper presents a comparative analysis of traditional edge detector operator with the proposed algorithm on grayscale images. The proposed algorithm is based on mathematical morphology and thresholding. Mathematical morphology is a new technique for edge detection based on set theory. It has been used for feature extraction and feature detection. Basic operations of Mathematical morphology are dilation, erosion, opening and closing. Based on these operations, experiment results are obtained using square structuring elements of different sizes with different images. The authors adopt the thresholding to change the brightness of edges of image. Detection of edge is a preprocessing step in image processing. Edge detection has been done using traditional operators (Canny, LoG, Prewitt and Sobel). The edge detection process had been used to reduce the amount of data and filter out useless information. The aim of this paper is to obtain the useful edges of the image object. This paper prefer round shape object images to extract the edges using different combination of structuring elements. Performance evaluation of the proposed algorithm is based on parameters like Root Mean Square Error (RMSE). This parameter is used to calculate the image quality of an output image. The experiment result shows that the proposed algorithm has more superior results than traditional operators.

A Novel Watermarking Approach based on Fuzzy Wavelets for High Authentication And Robustness

Vol. 3  Issue 2
Year: 2016
Issue:Apr-Jun
Title:A Novel Watermarking Approach based on Fuzzy Wavelets for High Authentication And Robustness
Author Name:U. Ravi Babu 
Synopsis:
The present study derives a novel scheme to embed the watermark for high authentication, robustness, security and copyright protection based on Fuzzy Wavelet. So far no researcher has attempted to use fuzzy logic in the spatial domain. The present paper developed a new technique called Fuzzy Wavelet (FW) approach for selecting the pixel locations to insert the watermark. The main aim of the proposed method is embedding the watermark image fully and to extract the watermarked image in an efficient manner. The approach is called Fuzzy Wavelet Region based Even, Odd (FWREO) method. The watermark bits are embedded in the pixel location selected by the FW approach by using the novel approach called Region based Even Odd (REO) method. The proposed method mainly consists of two steps. In step one, identify the pixel locations where the watermark is embedded. In the second step, REO method is applied on the FW approach of the first step. To find the effectiveness of the proposed method, the present method is tested on 24 images with the size of 512×512. The proposed gives comparative results when compared with other existing methods.

Feature Segmentation Of Blood Vessels Using Active Contour With Hybrid Region Information With Application To Retinal Images

Vol. 3  Issue 1
Year: 2016
Issue:Jan-Mar 
Title:Feature Segmentation Of Blood Vessels Using Active Contour With Hybrid Region Information With Application To Retinal Images
Author Name:Suri Sahithi and G. Guru Prasad
Synopsis:
Computerized detection of blood vessel structures is becoming one of the most interesting parts in the field of diagnosis of the vascular diseases. The objective of this paper is to introduce a novel filter, based on a new kernel function with Cauchy distribution to improve the accuracy of the automated retinal vessel detection. Moreover, for a good segmentation performance, the proposed model has the benefit of using distinct types of region information. The aim of the proposed model is to increase the accuracy of an image.

Analysis Of Iris Segmentation Using Circular Hough Transform And Daughman's Method

Vol. 3  Issue 1
Year: 2016
Issue:Jan-Mar 
Title:Analysis Of Iris Segmentation Using Circular Hough Transform And Daughman's Method
Author Name:Divya Ann Roy and Urmila S. Soni 
Synopsis:
Iris recognition is a special type of biometric system, which is used to identify a person by analyzing the patterns in the iris. It is used to recognize the human identity through the textural characteristics of one's iris muscular patterns. Although eye colour is dependent on heredity, iris is independent even in the twins. Out of various biometrics such as finger and hand geometry, face, ear and voice recognition, iris recognition has been proved to be one of the most accurate and reliable biometric modalities because of its high recognition. Iris recognition involves 5 major steps. Firstly, image acquisition is done in which the image is captured by a high resolution camera, then the iris and the pupillary boundary are extracted out from the whole eye image, which is called segmentation. After segmentation, the circular dimension is converted to a fixed rectangular dimension which is called normalization. From this normalised image, the feature is extracted from Gabor filter, DFT, FFT, etc. At last, the iris code is matched using Hamming distance and Euclidean method. This project focuses on iris segmentation. Iris segmentation is the most important part in the iris recognition process because the areas that are wrongly considered as the iris regions would corrupt the biometric templates resulting in a very poor recognition [16]-[21]. The main objective of iris segmentation is to separate the iris region from the pupil and sclera boundaries. There are various methods for segmenting the iris from an eye image to give a best segmented result. In this project, iris segmentation is done using Daugman's integro differential method and Circular Hough Transform to find out the pupil and the iris boundaries. Iris images are taken from the CASIA V4 database, and the iris segmentation is done using Matlab software where iris and pupilary boundaries are segmented out. The experimental result shows that 84% accuracy is obtained by segmenting the iris by Circular Hough Transform and 76% accuracy is obtained by segmenting the iris through Daughman's method. It is concluded that, the Circular Hough Transform method of iris recognition is more accurate than the Daughman's method.

Multispectral Image Compression With High Resolution Improved SPIHT For Testing Various Input Images

Vol. 3  Issue 1
Year: 2016
Issue:Jan-Mar 
Title:Multispectral Image Compression With High Resolution Improved SPIHT For Testing Various Input Images
Author Name:V. Bhagya Raju, K. Jaya Sankar, C. D. Naidu and Srinivas Bachu
Synopsis:
Due to the current development of Multispectral sensor technology, the use of Multispectral images has become more and more popular in recent years in remote sensing applications. This paper exploits the spectral and spatial redundancies that exist in different bands of multispectral images and effectively compresses these redundancies by means of a lossy compression method while preserving the crucial and vital spectral information of objects that prevails in the multispectral bands. In this paper, interpolated super resolution transform based DWT with Improved SPIHT algorithm for various multispectral datasets has been proposed. The proposed algorithm, a lossy multispectral image compression method yields better performance results for PSNR and Compression Ratio with sym8 wavelet when compared with previous well-known compression methods and existing discrete wavelets.

Hybrid Wavelet Based Approach for Image De-Noising Through PCA

Vol. 3  Issue 1
Year: 2016
Issue:Jan-Mar 
Title:Hybrid Wavelet Based Approach for Image De-Noising Through PCA
Author Name:Gunjan Sethi, Sukhvir Kaur and Jagdeep Singh
Synopsis:
De-Noising is a crucial problem for various types of image in the digital image processing. The main objective is to be fade away the noise factor by transfiguring into realistic Image as well as safeguarding the real quality and structure of the Image. Much hardware equipment such as digital electronic devices may suffer some issues that are noisy and blurred images due to degradation in the quality of the visioning image. These noisy images and blur images come under the problem of less information about the working object in a capturing environment. In this paper, the De-Noising technique has been proposed at different standard deviation for each processed image to check that at what level of noise it may work. In this proposed technique, wavelet is applied to a Noisy Image and further on the decomposed sections, SPG-PCA is used for quality enhancement. It consists of two stages: image estimation by removing the noise and further refinement of the first stage. Noise is removed at the maximum extent in first stage and the application of NPG improves the visualization of the De-Noised Image. A different standard deviation helps to optimize the original image which is based on the De-Noising scheme using quality matrices. The proposed technique can also be applied on satellite images, television pictures, medical images, etc. In this research work optimized De-Noising matrices like PSNR, SSIM, Maximum Difference and Normalized Cross-Correlation for the Dataset. Experimental results show a much improved performance of the proposed filters in the presence of Gaussian noise that are analyzed and illustrated.