Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. In this paper, we propose an ecient wavelet based algorithm for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. Hence this algorithm eectively uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the eciency of tumor detection. We also evaluate the eectiveness of proposed algorithm on varying the wavelet fusion parameters like number of decompositions, type of wavelet used for the decomposition. The experimental results of the simulation on MRI and CT images show the performance eciency of the proposed approach.
A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.
The theoretical tools of optical transformation and conformal mapping have enabled the transference of the concept of invisibility from the realms of mythology to scientific reality. A number of attempts have been made to achieve invisibility which relied on Nano or micro fabricated artificial composite material with spatially varying electro-magnetic properties. This approach limits the size of the invisibility region to a few wavelengths and is also very costly. Here, we experimentally solve this problem by designing a structure with low cost materials and simple manufacturing techniques based on the principles of refraction and lateral shift. This cloak developed is able to conceal macroscopic object of sizes of at least 3 orders of magnitude larger than the wavelength of light in all three dimensions. This clock can find huge application in defense and transformation optics.
Brain tumor, is one of the major causes for the increase in mortality among children and adults.Detecting the regions of brain is the major challenge in tumor detection.In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor.In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image.The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters.We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis.The performance efficiency of the algorithm is evaluated on the basis of PSNR values.The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization.The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.
Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop-in-place installations in the Arctic. The prototype selected will be field tested in Alaska in the summer of 2016.
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