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A brain tumor
based on
This pa...
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approach on
the Detection
of tumor and
cancer cells
Image processing is one of most ...
10. 8
Wavelet Based
Image Fusion
for Detection
of Brain Tumor
Brain tumor, is one of the major causes for the increase i...
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IEEE 2013 projects,M.Tech 2013 Projects,Final year Engineering Projects,Best student Projects,MS Projects,BE Projects,2013 2014 IEEE Projects


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IEEE 2013 projects,M.Tech 2013 Projects,Final year Engineering Projects,Best student Projects,MS Projects,BE Projects,2013 2014 IEEE Projects

  1. 1. Biomedical NO PRJ TITLE ABSTRACT DOMAIN YOP 1. 7 6 A brain tumor segmentation framework based on outlier detection This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium. contrast agent in the T1- weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect ab-normal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement. IEEE TRANSACT IONS on Biomedical 2013 2. 7 7 A Multi- Resolution Image Fusion Scheme for 2D Images based on Wavelet Transform The fusion of images is the process of combining two or more images into a single image retaining important features from each of the images. A scheme for fusion of multi-resolution 2D gray level images based on wavelet transform is presented in this paper. If the images are not already registered, a point-based registration, using affine transformation is performed prior to fusion. The images to be fused are first decomposed into sub images with different frequency and then information fusion is performed using these images under the proposed gradient and relative smoothness criterion. Finally these sub images are reconstructed into the result image with plentiful information. A quantitative measure of the degree of fusion is estimated by cross-correlation coefficient and comparison with some of the existing wavelet transform based image fusion techniques is carried out IEEE TRANSACT IONS on Biomedical 2013 3. 7 8 Brain Segmentation using Fuzzy C means clustering to detect tumour Region Tumor Segmentation from MRI data is an important but time consuming manual task performed by medical experts. The research which addresses the diseases of the brain in the field of the vision by computer is one of the challenges in recent times in medicine, the engineers and researchers recently launched challenges to carryout innovations of technology pointed in imagery. This paper focuses on a new algorithm for brain segmentation of MRI images by fuzzy C means algorithm to diagnose accurately the region of cancer. In the first step it proceeds by nioise filtering later applying FCM algorithm to segment only tumor area. In this research multiple MRI images of brain can be applied detection of glioma (tumor) growth by advanced diameter technique IEEE TRANSACT IONS on Biomedical 2013 4. 7 9 Detection of Epileptic Activity In The Human EEG- Based Wavelet Transforms Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures.Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analyzed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub bands. Statistical features Energy, Covariance are calculated for each sub-band. The extracted features are applied to Feed Forward Neural Network For system for classifications got classification accuracy of 98%. IEEE TRANSACT IONS on Biomedical 2013 #56, II Floor, Pushpagiri Complex, 17th Cross 8th Main, Opp Water Tank,Vijaynagar,Bangalore-560040. Website:, Email ID:, MOB: 9886173099 / 9986709224, PH : 080 -23208045 / 23207367 MATLAB – 2013 ((Image Processing, Wireless Sensor Network, Power Electronics, Signal Processing, Power System, Communication, Wireless communication, Geoscience & Remote sensing) )
  2. 2. 5. 8 0 Morphological image processing approach on the Detection of tumor and cancer cells Image processing is one of most growing research area these days and now it is very much integrated with the medical and biotechnology field. Image Processing can be used to analyze different medical and MRI images to get the abnormality in the image. This paper proposes an efficient K-means clustering algorithm under Morphological Image Processing (MIP). Medical Image segmentation deals with segmentation of tumor in CT and MR images for improved quality in medical diagnosis. It is an important process and a challenging problem due to noise presence in input images during image analysis. It is needed for applications involving estimation of the boundary of an object,classification of tissue abnormalities, shape analysis, contour detection. Segmentation determines as the process of dividing an image into disjoint homogeneous regions of a medical image. The amount of resources required to describe large set of data is simplified and is selected for tissue segmentation. In our paper, this segmentation is carried out using K-means clustering algorithm for better performance. This enhances the tumor IEEE TRANSACT IONS on Biomedical 2013 6. 8 1 EEG signal classification for Epilepsy Seizure Detection using Improved Approximate Entropy The result of the transient and unexpected electrical disturbance of the brain. About 50 million people worldwide have epilepsy, and nearly two out of every three new cases are discovered in developing countries. Epilepsy is more likely to occur in young children or people over the age of 65 years; however, it can occur at any age. The detection of epilepsy is possible by analyzing EEG signals. This paper, presents a hybrid technique to classification EEG signals for identification of epilepsy seizure. Proposed system is combination of multi-wavelet transform and artificial neural network. Approximate Entropy algorithm is enhanced (called as Improved Approximate Entropy: IApE) to measure irregularities present in the EEG signals. The proposed technique is implemented, tested and compared with existing method, based on performance indices such as sensitivity,specificity, accuracy parameters. EEG signals are classified as normal and epilepsy seizures with an accuracy of ~90%. IEEE TRANSACT IONS on Biomedical 2013 7. 8 2 Higuchi fractal dimension as a measure of analgesia Avoidance of patients’ intraoperative awareness and explicit recall of pain during surgery is important. Conventional methods of depth of anesthesia (DoA) monitoring involve physiological monitoring which are influenced by the administered anesthetic drugs. Balanced anesthesia is fusion of its four components analgesia, amnesia, motor blockade and hypnosis. One major component is analgesia which means inability to feel pain during surgery. Pain cannot be estimated any single physio- athological signal. A proper analgesia index proportional to the degree of pain experienced by the patient is required. Electroencephalogram (EEG) is a reliable means to determine real time DoA. In the present study, EEG of 12 volunteer subjects was recorded during relaxed and during pain. It was found that the Higuchi fractal dimension (HFD) feature of EEG from parietal region of brain reflects the sensation of pain and gives an overall accuracy of 95% in determining the pain experienced by the patient. IEEE TRANSACT IONS on Biomedical 2013 8. 8 3 Hybrid Dwt- Dct Coding Techniques for Medical Images In this paper, a hybrid image compression coding technique using the discrete cosine transform (DCT) and the discrete wavelet transform (DWT) is used for medical images. The aim is to achieve higher compression rates by applying different compression thresholds for LL and HH band wavelet coefficients. The DCT transform is applied on HL and LH bands with maintaining the quality of reconstructed images. After this, the image is quantized to calculate probability index for each unique quantity so as to find out the unique binary code for each unique symbol for their encoding IEEE TRANSACT IONS on Biomedical 2013 9. 8 4 A New Approach to Image Segmentation for Brain Tumor detection using Pillar K-means Algorithm This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order toimprove accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces. Experimental results clarify the effectiveness of our approach to improve the segmentation quality and accuracy aspects of computing time. IEEE TRANSACT IONS on Biomedical 2013
  3. 3. 10. 8 5 Wavelet Based Image Fusion for Detection of Brain Tumor 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 IEEE TRANSACT IONS on Biomedica 2013