Medical Practitioners on the Effects of Some Parameters on the Original Images – Radiology Example

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The paper "Medical Practitioners on the Effects of Some Parameters on the Original Images" is a great example of radiology research. In the medicine discipline, it is easy to study internal organs through x-ray images. However, there some elements that affect the quality and brightness of these images. Quality and brightness are important since they make it easy for medical professionals to the organs comprehensively. This report is detailed research on the effects of histogram equalization on the image, how varying the axes influence image quality, and effects of the dynamic range of inversion as well. Exercise 1 This laboratory part seeks to find the existing relationship between image and appearance.

The section also determines how histogram equalization affects these two aspects of the image. The figures below show how they change, with figure one representing the original image and figure the effects after histogram equalization. Fig: 1 fig: 2Figure (1) shows a CT-scan image. This is the scan of the upper part of an abdomen. The histogram chart is also embedding in the image. The image has a low contrast, which means that medics will find it unclear.

The anatomical structures and image details are difficult to differentiate. The histogram clearly proves that this image is of low contrast. From the histogram, pixels in grey level exist within the range of 40 and 20. The histogram has two peaks that help to determine its related structure. The First peak (70) that relates to thick body muscles is dark grey in color. At around 90, the second peak exists. It relates to liver and spleen organs making the image appear light grey as compared to outer muscles. When the histogram is applied to the same image (fig 2), the grey level range of colors spread and equalized.

Eventually, the image undergoes drastic changes from fig (1). For instance, it becomes clearer than before. The body organs, liver, and spleen become visible with their edges appearing. On the other hand, outer body muscles get darker and easily identified from the rest of the details since they have high density. In summary, image contrast changes coupled with slight noise. The histogram used clearly indicates a dynamic range that starts at 10 and ranging to 250 within the grey level.

This shows that pixel numbers in black and white grey increase. The final inference is that histogram equalization results in a higher image contrast as well as some noise (image). Exercise 2:Fig: (3.1) Fig: (3.1) Fig: (3.1)Y max=7000 X range= [-25,255]Changing of x value from -25,255 and y from y=7000 does not change the contrast of the image thereby retaining its original characteristics. The image contrast has been equalized therefore change in the pixel number (y) does not improve the image quality as well as its contrast.

The reason behind these constant results is that the grey level begins at zero for black up to 255 for white. Any alteration to the range level of grey never changes anything on the image quality as seen in the two histograms. In this experiment, histogram equalization makes it impossible for the changes in image contrast due to axes alteration. Exercise: 3Unlike the previous sections, this part aims to differentiate between the original image appearance and the after it has been inverted.

In addition, the distribution of the pixel of both images is differentiated. Fig 4.1 4.2Fig 4.3The two figures above show an MRI knee image. This report illustrates fig (4.1) is completely inverted to an opposite color as fig 4.2) shows. Black grey colors have changed to a lighter grey and white colors. At the same time, white and light grey change to black and deep grey colors. However, the grey level center remains the same as shown in figure 4.3. On the other hand, the original image's background changes as well.

Moreover, pixels distribution shows how the graph changes completely, taking an opposite direction. In the first figure (fig. 4.1), the graph depicts how most of these pixels exist within grey and white areas: 150-250. After inverting the image, the same number of pixels gets inverted thereby being compressed on dark grey and black areas within the range of 0-100. In simple terms, the process targets all the image pixels thus changing their original colors. They take the opposite direction. Finally, the paper infers that image quality or contrast cannot change by inversion.

Quality and contrast neither increase nor decreases these two image aspects. Exercise 4:Fig: (5.1) original Fig: (5.2)x1=10, x2=105 y1=40 , y2=0Fig: (5.3)fig 6.1 fig 6.2X1= 20, x2= 140 y1=30 , y2= 70 Fig 6.3Fig 7.1Fig 7.2 fig 7.3X1=20, x2= 200 y1=50, y2 =140This exercise strives to determine effects on the image when x and y values change for a pixel. In addition, it seeks to find out how these changes will affect the image and record the transformation that takes place. As figure (5.1) shows, the original image and its brightness.

It also shows the same image after altering these values to x1=10, x2=105, and y1=40, y2=0. In the end, the image is a bit darker. Changes in these values have affected the original image. The transformation graph (figure 5.3) shows the image had a pixel at grey level (10) originally and on the new image, it will be at (40). The pixel originally existing at (105) changes to black (0). Figures (6.2) and (7.2) follow the same process. For instance, figure 7.2 image had values at X1=20, x2= 200 y1=50, y2 =140.

Therefore, the original pixel was a color degree of 200 in the grey level and changed to 140. The pixel at 20 changed to 50 at the grey level. This process makes it possible to change and target any pixel carrying a certain grey level by easy increase or decrease in the y1 values and y2. It makes it darker or brighter. The process also targets certain pixels found in the original image carrying x1 and x2 values. Briefly, the transformation when applied to the whole pixel inverts the colors to the opposite like in exercise 3.

The process also targets pixels carrying colors of the grey level and increases or decreases color density. Exercise: 5Dynamic range compressionOriginal imageFig 8. 1 a=1Fig 8. 2 a=200Fig 8.3 a=100000This final exercise aims to show the final image appearance after the dynamic range compression. Compression takes place in a certain or specific area within the scale of grey level. Figure 8 is an illustration of the original image. It shows these pixels when compressed to zero areas (0) or darkness area.

The original image appears dark with unclear details as well as contrast if a=1. When the parameter changes to a=200, the image brightens with all the details clearer as the figure shows. Moreover, the histogram depicts how all pixels concentrate on the grey area. When this value increases to 100000, the image brightness increases significantly and some fogging is seen over this image. In summary, when the parameter (a) increases, the image brightness (window) also increases. However, the image contrast does not increase as seen in the histogram equalization. In summary, this report enlightens medical practitioners on the effects of some parameters on the original images.

These experiments are a clear understanding of what should be done to improve image quality and brightness. Exercise 1 is an experiment on how image histogram equalizes appearance and quality. However, the second exercise is an alteration on the values of the x and y-axis after image equalization. Exercise 3 is all about inversion on the image. When inverted, the color pixels changes in the opposite direction. It does not affect the image contrast in any way.

Exercise 4 is a detailed research on how changes in x and y values on the pixels affect image qualities. Finally, exercise 5 shows the final image after dynamic range compression, when parameters increase, the image brightness increases.

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