22-05-2012, 04:22 PM
An Artificial Neural Network for Detection of Biological Early Brain Cancer
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INTRODUCTION
A technique in which the data from an image are digitized and various mathematical operations are applied to the data, generally with a digital computer, in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the interpretation and recognition tasks usually performed by humans. Also known as picture processing. Manipulating data in the form of an image through several possible techniques. An image is usually interpreted as a two-dimensional array of brightness values, and is most familiarly represented by such patterns as those of a photographic print, slide, television screen, or movie screen. An image can be processed optically, or digitally with a computer.
General Working
The histogram equalization is operated on an image in three step: 1). Histogram Formation 2). New Intensity Values calculation for each Intensity Levels 3). Replace the previous Intensity values with the new intensity values. i) The given MRI is equalized using histogram. ii) The Histogram of an image represents the relative frequency of occurrences of pixel in a given image.
Feature Extraction
For the recognition of given query sample five invariant features are evaluated. For the evaluation of these features, the image is processed through :
•Histogram Equalization
•Binarization
•Morphological Operations
•Region Isolation
•Feature Extraction
The above stated methods are used for both query images & the database images.
Binarization
Image binarization converts an image of up to 256 gray levels to a black and white image. Frequently, binarization is used as a pre-processor before OCR. In fact, most OCR packages on the market work only on bi-level (black & white) images.
The simplest way to use image binarization is to choose a threshold value, and classify all pixels with values above this threshold as white, and all other pixels as black. The problem then is how to select the correct threshold. In many cases, finding one threshold compatible to the entire image is very difficult, and in many cases even impossible. Therefore, adaptive image binarization is needed where an optimal threshold is chosen for each image area.