digital radio graphic image enhancement for improved visualization

8/6/2019 Digital Radio Graphic Image Enhancement for Improved Visualization

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TechnoMoot 2011,COMSATS Institute of Information Technology, Abbottabad Pakistan, May 9 -10, 2011

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DIGITAL RADIOGRAPHIC IMAGE ENHANCEMENT FOR IMPROVEDVISUALIZATION

Nisar Ahmed, Waqas Ahmed and Sheikh M. ArshadHIEC University

Taxila Cantonment, Pakistan.E-Mail: [email protected]

ABSTRACT

Digital radiographic image enhancement techniques are used to accentuate and sharpen image features for enhanced visualization and better quality images. Image enhancement does not try to quash the artifacts it justmakes it easier to interpret by improving visualization. Different techniques are available to reduce visual noise,enhance edges and display improved contrast images. Digital radiographic images with low contrast, visual noisedue to electrical noise or X-ray scattering and blurring caused by complexity of body tissues, can worsen theresult of diagnostic. So it is necessary to enhance radiographic images to improve their visual quality, includingcontrast enhancement and feature enhancement. Different techniques to enhance digital radiographic imageshave been presented in this paper. Furthermore a graphical user interface has been made in MATLAB to showthe results of digital radiographic image enhancement techniques. The objective is to incorporate radiologists tooptimize the diagnostic quality of radiographic images.

KEYWORDSImage Processing, Image denoising, Image enhancement, Biomedical image processing, Biomedical imaging,Mammography, Medical diagnostic imaging, X-ray tomography .

INTRODUCTIONIn radiographic imaging, such as digital x-rays, CT scan or mammogram, images of different organs and tissuesare produced. There are many sources of interference in the production of radiographic images, such asinsufficient performance, noise of imaging device and the movement of patient. The quality of many images is

poor in their contrast, and to improve their quality for better visualization, to see clearly enough critical detailsand reduce the noise for diagnostic purposes, methods of enhancement are used. The purpose of imageenhancement is thus to improve a digital image quality and to support the human perception.

Image processing technology is used by planetary scientists to enhance images of celestial objects such as Mars,Venus, or other planets. Doctors use this technology to manipulate digital radiographic images such as CATscans, MRI images and digital X-rays. [1]

Diagnostic radiographic imaging is the medical assessment of body tissues and organs by means of static or dynamic radiologic images. Electromagnetic radiation in the form of ionizing radiation has been the predominantenergy source for diagnostic radiology.

The use of ionizing radiation in diagnostic radiology involves passing a localized beam of X-rays through the part of the body being examined. This produces a static image on film. The image, called a radiograph, or X-ray picture, can take several forms. It may be a plain radiograph, such as the common chest X ray; a mammogram,an X-ray image of the female breast used to scan for cancerous tumors; a tomograph, which produces an imageof the entire complexity of an anatomical structure with a series of X rays; or a computed tomography scan, acomputer analysis of a cross-sectional image of the body. [2]

Direct RadiographyIn this process the image is captured directly on the reusable storage phosphor plate and the image is transmitteddirectly to the computer. No intermediate steps or additional processes are required to capture the image. This


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process provides a direct feed from panel to imaging workstation. The two main types of direct radiology arediscussed hare.

Digital X-ray In X-ray image enhancement the process of enhancing edges progressively until optimal levels of clarity isreached is used. Basically the edge segments of the image are enhanced by making its light parts lighter and itsdark parts darker along the edges, preserving interior textures at their original gray level values.

Di gital Mammograms Mammography is currently the only proven and cost-effective method to detect early breast cancer. Amammographic examination generally contains four images, two views for each breast. These two views aredesigned to include most of the breast tissues within the X-ray images. In mammography a radiologist firstscreens the mammograms for abnormalities. If a suspicious abnormality is detected, further diagnostic workup isthen performed to estimate the likelihood that the abnormality is malignant. [3]. It is very difficult to interpret theX-ray mammograms because of the small differences in image density of various breast tissues. The individual

particle of microcalcifications may be under 0.5mm in diameter with irregular and heterogeneous shape.Therefore careful diagnosis should be performed for the clustered microcalcifications that may cause an earlystage cancer. [4]. Different image enhancement techniques have been applied on digital mammogram to improvethe visibility of mammographic lesions on a computer monitor.

CT scansComputed Tomography scans are special x-ray tests that produce cross-sectional images of the body using x-raysand a computer. These images allow the radiologist to look at the inside of the body. This type of special x-ray,in a sense, takes "pictures" of slices of the body so doctors can look right at the area of interest. CT scans arefrequently used to evaluate the brain, neck, spine, chest, abdomen, pelvis, and sinuses.

CONTRAST ENHANCEMENTImage Contrast is the difference in appearance of two or more parts of an image seen simultaneously. An imagemust have good brightness contrast for proper vision. In a low contrast image we cant distinguish clearly

between different objects. Increasing the contrast makes the light areas become lighter and dark areas becomedarker. Contrast enhancement increases the visual perception of difference between different parts of an image.We use different techniques of histogram modification to improve the visual contrast of the image. Thehistogram of an image with the intensity levels in the range [0, L-1] is a discrete function.

( )

Where

sk is the intensity value. Nk is the number of pixels in the image with intensity sk.

f(sk) is the histogram of the digital image with Gray Level sk Linear Contrast StretchingIn linear histogram stretching the histogram of image matrix is linearly stretched over the entire range of spectrum. This technique maps the intensities to new values such that the data is stretched to the whole range.This technique produces useful results when the histogram of original image is concentrated in a narrow range of spectrum. The equation used for linear contrast stretching is equation of line. Whereas m is gradient and c is yintercept.

Histogram EqualizationHistogram equalization generates a gray map which changes the histogram of the image. It redistributes all

pixels values such as to produce uniform histogram. This technique is especially important when the usable data


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is represented by close contrast values. Histogram equalization spread out the most frequent intensity values toallow the areas of lower contrast to gain a higher contrast.

The principle disadvantages with histogram equalization are:-

The histogram equalization method may result in over enhancement and saturation artifacts. Histogram equalization can be found on the fact that it may significantly alter the brightness of an

image. The histogram equalization method does not take the mean brightness of an image into account. The

mean brightness of the histogram-equalized image is always the middle gray level regardless of theinput mean.

Brightness Preserving Histogram EqualizationBPHE (Brightness preserving histogram equalization) is used to overcome the problem with simple histogram

equalization. It compute the mean of the image and decomposes the image into two sub images based on themean of the image. One of them is set of samples less than or equal to the mean whereas the other is the set of samples greater than the mean. Then the technique equalizes the sub images independently based on their respective histograms with the restriction that the samples in the first sub-image are mapped into the range fromthe minimum gray level to the input mean and the samples in the latter sub-image are mapped into the rangefrom the mean to the maximum gray level. Thus the resulting equalized sub images are bounded by each other around the mean, which has an effect of preserving mean brightness.

Contrast Limited Adaptive Histogram Equalization

CLAHE (Contrast limited adaptive histogram equalization) computes multiple histograms, each correspondingto a distinct section to increase local contrast, rather than overall contrast. The image is divided into tiles and itoperates on tiles rather than the entire image. Contrast of each tile is enhanced by histogram equalization andthen all the tiles are combined using bilinear interpolation to eliminate the artificially induced boundaries. The

contrast especially in homogeneous areas is limited to avoid noise amplification.

REMOVING NOISERadiographic images are prone to a variety of types of noise. Noise is the result of errors in the image acquisition

process that result in pixel values that do not reflect the true intensities of the real scene. Noise can occur indigital radiographic images due to several reasons such as

If the image is scanned from an X-Ray film or CT image, the film grain is a source of noise. It can be aresult of a damaged film or due to the scanner i tself.

If the image is captured directly from digital X-Ray scanner or a CT scanner it can be due to mechanismof gathering the data.

Sigma FilterLinear Filtering is easiest method to remove certain type of noise. Averaging or Gaussian filter can be used toaccomplish this job. In averaging filter each pixel gets set to the average of its neighboring pixels. The problemwith averaging filter is that edges of image get blurred. To overcome this problem there are some selectivetechniques. Sigma filter is a selective mean filter. It preserves edges better and is less sensitive to outliers. Thefilter smoothes an image by taking an average over the neighboring pixels, but only includes those pixels thathave a value not deviating from the current pixel by more than a given range. Outliers having a value verydifferent from the surrounding are not included in the average and, thus, completely eliminated from blurring.

Median FilterMedian filter works in a similar way as averaging filter, the only difference is the output value of a pixel isdetermined by the median of the neighboring pixel rather than mean. The principle advantage of median filteringover averaging is that it is much less sensitive to extreme values. Therefore median filtering is better able toremove noise without blurring the edges.


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Figure 3. Application of contrast enhancement techniques on Digital X-Ray of hand and Neck and Mammogram.

Figure 4. Contrast Enhancement result onCT Scan image.

Figure 5. Noise reduction results onFigure.4(c) image.


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Table 1: Contrast Enhancement

Advantages Disadvantages Linear Contrast Can produce good result by linear

stretching.Cant produce much attractive resultsin many cases.

HE This technique is best for visual perception especially when image haveclose contrast data.

This technique may result in brightnessshift because it does not take mean

brightness.CLAHE This technique produces good results

when histogram equalization cant produce attractive results.

This technique produce limited contrastenhancement due to local enhancement.

BPHE Produce best results when HE produces brightness shift.

Take more time duce to separation intotwo images and appending after their enhancement.

The best among the above discussed technique is BPHE (brightness preserving histogram equalization) it produces good result while preserving the image mean brightness.

Table 2: Noise Reduction

Advantages Disadvantages Median Filtering Easy to implement. Image edges get blurred.Sigma Filter Easy to implement by adding threshold

in averaging filter.Cant be used for salt & pepper noise.

Wiener Filter Produce best output when the noise isadditive white Gaussian noise.

Require high computational time.

Sigma filter produce better result for CT and MRI images. It preserves the edges while removing the noise.Radios and threshold can be adjusted to acquire the desired performance. However median filter also reducenoise effectively. Its results become good if we apply image sharpening filter after median filtering.

CONCLUSIONContrast enhancement using histogram processing is an effective method, four techniques of histogram

processing has been applied on a large number of digital radiographic images. BPHE has shown best resultsamong the four. Three techniques have been used for noise reduction and sigma filter has shown better resultamong them. However median filter followed by image sharpening also show good results.

REFERENCES[1] Image Processing and Image Enhancement, Dr. William L. Joyner, East Tennessee State University Johnson

City, Texas, 1996[2] Microsoft Encarta 2009. 1993-2008 Microsoft Corporation. [3] Medical Image Analysis Methods, Edited By Lena Costaridou Published By Taylor and Francis Group

[4] Adaptive Mammographic Image Enhancement Using First Derivative and Local Statistics, Jong Kook Kim, JeongMi Park, KounSik Song, and Hyun Wook Park, IEEE TRANSACTIONS ON MEDICALIMAGING, VOL. 16, NO. 5, OCTOBER 1997

[5] Anisotropic diffusion, Perona and Malik in 1987[6] The Application of Image Enhancement on Color and Grayscale Images, Nisar Ahmed, Waqas Ahmed,

HITEC University Taxila, All Pakistan Technical Paper Competition, CIIT Lahore.[7] Digital Applications of Radiography, Ramesh J. Patel, Qatargas Operating Company Limited, Doha Qatar,

3rd MENDT - Middle East Nondestructive Testing Conference & Exhibition - 27-30 Nov 2005 Bahrain,Manama .

[8] Enhancement of Images Using Histogram Processing Techniques, Komal Vij & Yaduvir Singh, Thapar University, Patiala, India.

[9] Radiographic Image Enhancement, M Analoui, Oral and Maxillofacial Imaging Research Facility, School of Dentistry, Indiana University, Indiana, USA

[10] Application of Image Enhancement Techniques to Magnetic Resonance Imaging, Michael L. W ood, Val M.Runge, The Radiographical Society of North America.

digital radio graphic image enhancement for improved visualization

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