We use the image enhancement techniques to accelerate the faster disease identification and diagnosis of malignant tumors. In The paper a method has presented to enhance the image in in the Wavelet field. If the compression is done on the wavelet field, it increases very rapidly it. Currently the Breast cancer is one of the most common cancers among women and is one of the causes of their death. New imaging technology runs today a valuable role in the analysis and treatment. Mammogram is an X-ray examination of the breast that is used to identify breast diseases. In Mammography, how to enhance the image is very important to investigate mammogram accurately. Image enhancement algorithms in the field of Wavelet is a direct enhancement method of contrast. it has been introduced by Dhawan et al to complete the method of Gordon Ranagayyan. This algorithm is very efficient in detecting malignant tumors. Image enhancement algorithm is a direct method to enhance the contrast.
We use the wavelet basis functions in signal processing for signals analysis and images that have focused or transient components at a same time or place. In the past decades the use of wavelet transformation has grown so much for image processing. In The wavelet transformation in image processing, the image analysis is conducted on different scales and we use this feature of Wavelet / multi-scale transformation in Mammograms. We use recognize the larger scales in recognizing larger tumors. In this algorithm, the theory Mallat is used to analyze the multi-resolution Wavelet (Figure 1).
In This step the obtained wavelet coefficients was used. We define an index for image contrast anywhere. As a result, the resulting image from the reconstructed new coefficients will be better images.
Due to the unavailability of high quality images in the article, we will explain the image differences derived from simulation and available images. Also change of the image format as jpg also destroy some data. , But the result of the simulation shows that via decomposition of images, as shown above, the wavelet coefficients abrupt changes – it represents the boundary tumor within the breast tissue. – will be detectable as Full (Refer to Photos A3-4-5-6).
We can see that – according to the resulting pictures –using λ and its value by a parameter, we can control the contrast of the acquired image and the image resolution. (See pictures 9-8).
Contrast enhancement is one of the important branches in diagnosis of mammographic images. Several methods have been reported to enhance the contrast mammograms that they fall into two main categories. The first is the image direct enhancement algorithms and the second is the image indirect enhancement algorithms. To enhance the image directly, adjustment and change are exactly done on image contrast. But in the indirect algorithm, improving the contrast is done indirectly, such as balancing the histogram.
In the paper, a direct method has been used to enhance the mammography image contrast. Algorithm is presented based on image multi- scale Division in wavelet field. Consequently, we can enhance the required details in different scales.
The Provided Technique has high diagnostic accuracy in mammography images. As a result, in the early stages of cancer that the gland is very small, the use of this method can be very effective in raising the possibility of prompt diagnosis and treatment.
In Defined functions in MATLAB, algorithm implementation of a multi- resolution wavelet transformation is carried out on the image by MATLAB Based on type of Wavelet Haar wavelet and transformation of stage 4 wavelet (Refer to function 3.1).
In Calculation of the Haar wavelet transformation, this function takes the wavelet transform coefficients, and that function gives the input signal. Where we need to display the output, we can use the function A.
In calculation of contrast index C, the first entry of the function is one of coefficients detail and the second entry is coefficients app. Its output is C (Function 4)
In The enhanced coefficients. The function output is the new coefficients with application of landa and its input is original coefficients and landa value (Function 5). In the calculation of local contrast in a particular area of the image see to picture 6.