Abstract

Image processing is a process of converting an image into digital form and achieve some maneuvers on it, in order to get an enhanced image or to mine some useful information from it. It is a type of signal dispensation in which the input is an image, like video frame or photograph and output, may be image along with its characteristics and features associated with that image. An image is defined as a two-dimensional function F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x, y, and amplitude values of F are finite, we call it a digital image. Image processing mainly consists of three basic steps. They are as follows: Initially, the image will be imported by using an optical scanner or by high-digital photography. Then the captured image will be subjected to the analyzation and manipulation process. These process also includes compression of data, enhancement of the image and spotting the patterns that are not visible to human eyes like satellite photography. Finally, the output will be obtained as an alternative image or any other essential feature extraction of the pre-processed image. Image Processing consists of two major types. They are  Analog Image Processing and Digital Image Processing. Digital Image Processing is a process in which a digital system is developed for processing a digital image and extracting feature form of results. Digital Image Processing works on the basis of an algorithm. An Intelligent System for Accurate Detection and Prediction of Alzheimer’s Disease mainly uses the k-nearest neighbor algorithm. Alzheimer’s  Disease is a type of disease in which the brain cells tend to die away and cause memory loss. In our proposed model we predict the accuracy of the amount of memory loss occurred in an affected brain. This system is mainly developed for helping the doctors and psychologists to obtain a maximum level of accuracy of the patient’s affected brain.

Keywords

Image, Image Processing, Digital Image Processing, Alzheimer Disease, Pre-Processing, Analyzation, Manipulation, Wavelet Transformation, KNN Algorithm, Feature Extraction, Accuracy,

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References

  1. Andrea Rueda, Fabio A. González and Eduardo Romero,(2014)’ Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases’,IEEE Transactions On Medical Imaging, No. 6, Vol. 33
  2. Guler I, Ubeyli E.D.(2005) ’Feature Extraction from Doppler Ultrasound Signals for Automated Diagnostic Systems’ Computers in Biology and Medicine;.35(9): 735–764
  3. Georgiadis,Cavouras D, Kalatzis I, Daskalakis A, Kagadis G.C, Sifaki K, Malamas M, Nikiforidis G, Solomou E.(2008)‘ Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features’,Computer Methods and program in biomedicine, vol 89, pp24-32.
  4. Tejal A. Fuse1 ,Nikita D. Jayasignpure and Prof. Pragati D. Pawar,(2014),’Nmf-Svm Based Cad Tool For The Diagnosis Of Alzheimer’s Disease ‘,Volume 3, Issue 12, December.
  5. Zhang D, Wang Y, Zhou L, Yuan H,(2011),’ Multimodal Classification Of Alzheimer's Disease And Mild CognitiveImpairment’.Neuroimage.,Apr 1;55(3):856-67.
  6. For Classification Problems By Mutual Information Based On Parzen Window’, IEEE Transactions on Neural Networks, 13(1), 143–159.
  7. Kwak, Nojun,(2002)’Input Feature Selection For Classification Problems’,IEEE Transactions on Neural Networks, 13(1), 143–159.