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Brain Tumour Extraction from MRI Images Using MATL
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Medical image processing is the most challenging & nbsp; and the emerging field is now a day. MRI image processing is part of the & nbsp; field. This paper introduces the proposed & nbsp; strategy to detect and extract brain tumors from patients & nbsp; MRI scan images of the brain. This method uses some noise elimination functions, segmentation and morphological operation, which are the basic concepts of image processing. &Detection and extraction of tumors from MRI scan images of brain is accomplished by using MATLAB software.
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A Lossy Coding Scheme for Images by Using The Daub
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Abstract - this paper introduces the use of Daubechies wavelet transform - D4 and the theory of the intersection region of the whole cross point regions (LS HWT & ICRs), by image lossy coding intersection 2D. The basis of this argument is that graycoding's therapeutic span is the neighbor's gray level of 2n points. Before gray code adjacent dataset ofcross point is determined, it is called whole intersection region (ECR). After gray coding, these regionsalways contain only 1 bit or each bit plane depending on the number, and 0 bit plane is decomposed. The optimization of probability in each bit plane plays an important role in lossless image data transmission during encoding and decoding. We will show how Daubechies wavelet transform combines the theory of the whole cross point region to become a lossy coded image. The goal of this method is to create a lossy coding scheme with high compression ratio and low distortion coefficient compared with some other methods. Finally, some preliminary results show the application of Daubechies matrix in face images.
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