Mukund N Naagund

Bio: Mukund N Naagund is an academic researcher from Kuvempu University. The author has contributed to research in topics: Histogram equalization & Peak signal-to-noise ratio. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Image contrast enhancement using DWT-SVD based masking technique

Abstract: A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.

Enhancement of Veterinary Vaginoscopic Image of Female Canines using Color Transfer Algorithm in 1αβ Color Space

Abstract: Veterinarians need knowledge of the estrus stage of the reproductive cycle to decide the exact mating time for canine breeding. Through vaginoscopy, a visual examination of the vaginal vault of female canines can be done and the mating period can also be determined. However, due to low light illumination in vaginoscopic examination, the captured images lack visual quality. This hampers accurate determination of the mating period. In this study, an attempt has been made to enhance vaginoscopic images of female canines recorded at the College of Veterinary & Animal Sciences, Mannuthy, Kerala, India. These captured images are subjected to singular value decomposition (SVD), with the aid of a color transfer algorithm in 1αβ cone space. SVD results in a singular value matrix that accommodates the majority of the intensity information of the input image. By scaling these singular values, the total intensity of the input image is improved. Further, color transfer in 1αβ cone space is carried out to get better enhanced images. These enhanced images are evaluated using various quality parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy and mean. The proposed algorithm has an average MSE of 20.11, while PSNR, entropy and mean values are 35.65 dB, 7.16, and 104.15, respectively. The qualitative analysis shows that this algorithm can be used to enhance the veterinary vaginoscopic images intended for further processing. This algorithm facilitates veterinarians to clearly visualize the wrinkles and bloody discharge during oestrus cycle.

Image Contrast Enhancement by Scaling Reconstructed Approximation Coefficients using SVD Combined Masking Technique

Abstract: The proposed method addresses the general issues of image contrast enhancement. The input image is enhanced by incorporating discrete wavelet transform, singular value decomposition, standard intensity deviation based clipped sub image histogram equalization and masking technique. In this method, low pass filtered coefficients of wavelet and its scaled version undergoes masking approach. The scale value is obtained using singular value decomposition between reconstructed approximation coefficients and standard intensity deviation based clipped sub image histogram equalization image. The masking image is added to the original image to produce a maximum contrast-enhanced image. The supremacy of the proposed method tested over other methods. The qualitative and quantitative analysis is used to justify the performance of the proposed method.

Region of Interest based Contrast Enhancement Techniques for CT Image

Abstract: Medical images are used for decision making purpose by the physicians to recognize the disease by which the patient is suffering. Thus to lead the highly reliable results, it is mandatory that the image should be more qualitative and informative. In order to make an image more qualitative, the image enhancement technique is applied to it. Image enhancement is such a vast field and covers a large number of enhancing mechanisms under it. This study develops a medical image contrast enhancement technique by applying multi filters, curvelet transformation and un-sharp masking. For Multi Filtration, the Kuwahara filter and Gaussian filter is used in this. Along with this the ROI is applied to detect the infected areas or noise from the image. The implementation results show that the final enhanced image is quite qualitative in comparison to the initially input image in terms of Bit Error Rate, Mean Square Error, Standard Deviation, Peak Signal to Noise Ratio, Entropy, Contrast to Noise Ratio etc.

Medical Image Enhancement In Health Care Applications Using Modified Sun Flower Optimization

Abstract: Image enhancement (IE) is a process which improves the contrast of image by sharpening the edge pixels intensity. This technique has attained much attention in medical field and several enhancement techniques are proposed by researchers. In image processing, the enhancement is regarded as complex optimization issues. This work introduces an efficient model to solve optimization issues using a modified optimization approach. Initially, the input medical images are denoised using Modified median filter (MMF) filter. Then these denoised images are enhanced for the further process. The enhancement is carried out by pixel intensity of image. The parameters like entropy, edge information and intensity are optimized by modified sun flower optimization (MSFO). This optimization is used for increasing the convergence speed. The overall evaluation is carried in Matlab platform. The image quality is analyzed on six performance metrics and compared over several approaches and provided better results. The experimentation is evaluated on five medical images and the Mean square error (MSE) and peak signal noise ratio (PSNR) achieved by the medical image 1 are 0.02 and 43.7 respectively.