Study of Noise Detection and Noise Removal Techniques in Medical Images

Published Online March 2012 in MECS

Median Filtering:

Median filtering is similar to using an averaging filter, in that each pixel is set to an ‘average’ of the pixel values in the neighborhood of the corresponding input pixels. However with median filtering, the value of an output pixel is determined by the median of the neighborhood pixels, rather than the mean. The median is much less sensitive than the mean to extreme values. Median filtering is therefore better able to remove this outlier without reducing the sharpness of the image.

Adaptive Filtering:

The wiener2 function applies a Wiener filter which is a type of linear filter to an image adaptively, tailoring itself to local image variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing. This approach often produces better result than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high frequency parts of an image. In addition, there are no design tasks; the wiener2 function handles all preliminary computations, and implements the filter for an input image. Wiener2, however, does require more computations time than linear filtering. Wiener2 works best when the noise is constant-power (“white”) additive noise, such as Gaussian noise.

Adaptive median filter:

Adaptive median filtering can handle impulse noise with probabilities. The adaptive median filter is that it seeks to preserve detail while smoothing non impulse noise, something that the traditional median filter does not do.

• II-    METHODOLOGY

Figure shows the different noise which has find in these medical image and after applying the filtering techniques in these medical images and same has been given through histogram.

Table 1.1

Image	Original Image		Noisy Image		Filtered Image
	Std	Mean	Std	Mean	Std	Mean
MRI	70.0623	182.2473	74.0267	180.0172	68.8923	182.5818
Cancer	61.2939	62.4918	64.0972	63.9113	53.1813	60.3204
X-Ray	65.4542	145.47576	68.2635	144.9646	64.8242	145.9382
Brain	91.0872	85.9561	92.5692	87.1018	90.5972	85.7115

(a)Original MRI image

(c) Applying Median Filter

(a) Original x-ray image

(c) Applying median Filter

(a)Original cancer image

(c) Applying median Filter

(a)Original image (Brain image)

(c) Applying Median Filter

(a) Original x-ray image

(c) Applying median Filter

(a)Original image (Brain image)

(b) ROI generated for noisy image

(c) Applying Median Filter

Fig1.1.1 Shows finding the salt and pepper noise in MRI, Cancer, X-ray, Brain images and applying the median filter on these images.

(c ) Histogram for noisy image

The following figure shows the noise pattern for the MRI, Cancer, X-ray and brain images. In these medical images after finding the salt and pepper noise we have taken a region of interest for noisy images and the histogram shows the noise pattern that is it is the salt and pepper noise.

(f) ROI for the noisy image

(h) histogram for ROI

(g) Histogram for the noisy image

(j) ROI for noisy image

(n) ROI of noisy image

0           50          100          150         200         250

(k) Histogram for the noisy image

(o) Histogram for the noisy image

(l) Histogram for the ROI

(p) histogram for the ROI

After finding the speckle noise in MRI, Cancer, X-ray and brain images and applying the median filter for these images.

Table 4.6

Image	Original Image		Noisy Image		Filtered Image
	Std	Mean	Std	Mean	Std	Mean
MRI	70.0623	182.2473	74.0267	180.0172	65.7016	179.8383
Cancer	61.2939	62.4918	64.0972	63.9113	55.4636	63.7529
X-Ray	65.4542	145.47576	68.2635	144.9646	65.4814	144.7277
Brain	91.0872	85.9561	92.5692	87.1018	89.1596	87.1165

(a)Original Cancer image

(a)Original MRI image

(c) Applying Adaptive Filter

(c) Applying Adaptive Filter

(a) Original x-ray image

(c) applying Adaptive Filter

Table 4.10

Image	Original Image		Noisy Image		Filtered Image
	Std	Mean	Std	Mean	Std	Mean
MRI	70.0623	182.2473	74.0267	180.0172	0.2518	0.6999
Cancer	61.2939	62.4918	64.0972	63.9113	0.2073	0.2446
X-Ray	65.4542	145.47576	68.2635	144.9646	0.2409	0.5613
Brain	91.0872	85.9561	92.5692	87.1018	0.6649	0.3414

(a)Original image(Brain image)

(a) Original MRI image

(c) Applying Adaptive Filter

(c) Applying Average

Fig4.6.1 Shows finding the salt and pepper noise in MRI, Cancer, X-ray, Brain images and applying the adaptive filter on these images.

After finding the salt and pepper noise and applying the average filter on these images.

(a) Original Cancer image

(c) Applying Average Filter

(c) Applying average Filter

(a)Original image (Brain image)

(a) Original x-ray image

Fig4.10.1 Shows finding the salt and pepper noise in MRI, Cancer, X-ray, Brain images and applying the average filter on these images.

• III-    DISCUSSION:

As per discussed in Different medical images like MRI, Cancer, x-ray and brain images have been studied. After finding the salt and pepper noise in MRI image various filtering techniques have been applied and it is found that the adaptive filter works better for the noisy image. The standard derivation for the noisy image is 74.0267 and the standard derivation for the adaptive filtered image is 65.7016.

After finding the salt and pepper noise in Cancer image various filtering techniques have been applied and it is found that the median filter works better for the noisy image. The standard derivation for the noisy image is 64.0972 and the standard derivation for the adaptive filtered image is 53.1813.

After finding the salt and pepper noise in X-ray image various filtering techniques have been applied and it is found that the median filter works better for the noisy image. The standard derivation for the noisy image is 68.2635 and the standard derivation for the adaptive filtered image is 64.8242.

After finding the salt and pepper noise in Brain image various filtering techniques have been applied and it is found that the adaptive filter works better for the noisy image. The standard derivation for the noisy image is 92.5692 and the standard derivation for the adaptive filtered image is 89.1596

• IV-    CONCLUSION: