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Rathore, Yogesh
- An Efficient and Useful Hybrid Approach for Detection of Lung Cancer
Authors
1 Raipur Institute of Technology, Raipur, (Chhattisgarh), IN
Source
Research Journal of Engineering and Technology, Vol 2, No 4 (2011), Pagination: 199-202Abstract
Analysis of medical images is often intricate and time intense, even for experienced physicians. The aid of image scrutiny and machine learning can make this process easier.. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier recognition and treatment stages, in which the time issue is very vital to determine the infection in the patient as possible as fast, especially in various cancer tumors such as the lung cancer, breast cancer. Lung cancer images passed basic three stages to achieve more quality and accuracy in our experimental results, firstly image enhancement stage which is low pre-processing image techniques. Gabor filter, using a Gaussian rule in which produced the preeminent resultant enhanced images, In the image segmentation stage, thresholding segmentation mechanism by marker using the gradient magnitude as the segmentation function and computed the watershed renovate of the segmentation function. Finally features which assist to make a contrast between normal and abnormal images were. Two features computed were: black and white pixels percentage of the input image and the second feature is image Masking and tagging.Keywords
Lung Cancer, Enhancement, Segmentation.
- An Effective Method for Transferring Color to Gray Scale Image Using Luminance Matching without Human Intervention
Authors
1 Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur (C.G.) 492101, IN
Source
Research Journal of Engineering and Technology, Vol 1, No 1 (2010), Pagination: 47-49Abstract
In this paper, we are proposing a fully automatic method for adding colors to grayscale images. In contrast to many previous computer-aided colorizing methods, which require intensive and accurate human intervention, this method needs only the user to provide a target gray level image for the process of 'colorization', a colorful image of the similar content as the grayscale image is automatically retrieved from the database of images, as an input source image. Then, the source and target image are both transformed into a perceptually de-correlated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Once a best matching source pixel is found, its chromaticity values are assigned (transferred) to the target pixel while the original luminance value of the target pixel is retained. The only requirement of the method is that the compositions of the source and target scenes resemble each other.Keywords
Image Coloring, Image Retrieval, Color Transfer, Luminance Matching.- An Efficient Method for Image Matching and Retrieval
Authors
1 National Informatics Center Services Inc., Raipur (C.G.), IN
Source
Research Journal of Engineering and Technology, Vol 1, No 2 (2010), Pagination: 74-78Abstract
In this paper, we are proposing an algorithm for a content-based method for retrieving images. In contrast to many previous computer-aided methods, which require intensive and accurate human intervention, this method needs only the user to provide a image database of the similar content as the query image.
For input, the method requires a true color source image database and a target image. The source and target image are both transformed into a perceptually de-correlated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Along with the luminance distribution, high pass filter is used toimprove texture information in L-channel of the source and target imagesand edge detector is used in order to detect the edges of the source and target images. The only requirement of the method is that the compositions of the source and target scenes resemble each other.