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The formation of images from radiography is essentially a map- ping of photons through the object which comprised of different
densities, such as organs and bones. As an example, cancerous cells can be identified as these cells show distinctively different
shades of grey compared to other structures. Figure 4.20 shows an image captured using radiography where darker masses can
be observed on the left of patient’s lung cavity which indicates the degradation of internal lung cells. This is a condition called
spontaneous pneumothorax induced by pneumonia. Without this image, it would be impossible to identify the condition and
to prescribe the correct diagnosis. The degradation of the left lung cells is compared to the right lung which looks lighter and
normal. However, it must be ensured that all data must remain intact during image transfer as the loss of any information will
halt the effort of diagnosis. Analogue images can be observed everywhere in the surrounding environment. This is particularly
true as our observation and interpretation of the surroundings is comprised of a collection of various spectrums with uncounta-
ble distinctive features, and it is not possible to transmit images with infinite features. Hence, to enable an image to be transmit-
ted, it has to be converted to a finite or exact size, such as the bitmap arrays of colored or grey dots known as pixels. Next,
to transmit an image, a network bandwidth with sufficient data rate is required. For instant, a considerable sized image with
3000 × 2000 pixels at 6 megapixels of resolution and 256 dif- ferent grey tones will contain the size as shown below:
4.8
4.9
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It excludes insignificant features such as check errors and sur- plus data of the image, which include the types of file and date
when image is taken. From the equation, b represents the amount of bits per pixel which results in the intensity of colors,
H represents the height and W represents the width. Using the value of pixels given above, the total uncompressed bitmap size
of 5.72MB is obtained. It is a fairly simple calculation; first the number of pixels is obtained by multiplying H with W. Next,
the unit is converted to bytes through the division of the value with eight binary bits. Finally, the value is expressed in kilobyte
KB by dividing the value by 1024. To express the value in megabytes MB, the value has to be divided twice by 1024.
This calculation exhibits the amount of data in managing digital images, and can easily be reduced for the ease of transmittance.
4.2.3 Compression Image
To ensure an efficient transmission and in order to reduce the space required for storing, images are compressed and shrunk.
This is known as a loss compression. However, there will be some loss of data when the compressed image is decompressed
back to its initial size due to the loss of some algorithms. This can be prevented via lossless compression. Using this form of
compression, the decompressed image will have the exact prop- erties as its original form. Take note that a digital photo is con-
structed of matrix of pixels with strings of 0 and 1. The evaluation of this topic will start by analyzing the advantages
and disadvantages of the two compression methods Tobin, 2001, seeram et al 2008. It is necessary to compress diagnosis
images to ensure its efficient transfer via medical channels and to decrease the amount of space needed for data storage, thus
lower the operating cost. In digital photos, primary colors with consisted of different intensities of reds, greens and blues are
used to construct the pixels. These colors however do not in- clude secondary or subtractive colors like cyan, yellow and ma-
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genta which are resulted from the mixture of any two of the primary colors. The constitution of colored pixels in digital im-
ages is identical for other digital illustrations in different gadg- ets such as camera, mobile phone, television and computer. The
addition of different intensities of primary results resulted in enormous possibilities of shades known as additive colors. The
pixels in a regular color bitmap are comprised of 3 numbers each one byte for one primary color which resulted in differ-
ent densities of colors constructed from the primary colors in overall. Therefore, a total of 24 bits are required to form one
pixel 1 byte equals to 8 bits for storing the color data. To get an idea about the enormous possibilities of color formations, an
equation as shown below is used to calculate the number of col- or formations:
= 16 777 216 more than 16 million of pos-
sibilities. In computer language, digital images with 24 colour bits are called true colour images.
From the equation, b represents bit-depth or bits number. A camera with between 12 and 14 bits is required to produce im-
ages with higher intensity of colors. To compress images, re- gions with identical colors are identified and regions that
contain gaps and unnecessary fields are eliminated. In telemedi- cine, the compression of images using loss compression is not
acceptable as loss of information cannot be tolerated. This is especially true as the identical shades of grey may marked as
identical regions during compression, and some regions may be eliminated. For most diagnosis images, abnormalities present in
the body may not be distinctive or refined enough to be identi- fied even though it is absolutely crucial to the patient’s condi-
tion. This may be due to conditions such as the initial stage of cancer of abnormal foetus formation, and the use of lossy com-
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pression might cause the elimination of crucial information. The strength of image is widely known as quality factor. The com-
parison of different compressing ratios with an original MRI image is shown in Figure 4.21. In a, the image after a being
compressed at 1:20 is shown and b shows the compression at 1:100. Using careful observation, the image in b is noticeably
clear while the other image appears more blurry.
Figure 4.21 MRI scanned image. a Without data compres- sion FONG et al 2011
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Figure 4.21 Continuation b moderate compression of 1:20; c compressed to 1:100 FONG et al 2011
4.2.4 Bio-potential Electrode Sensing
To measure the rate of heartbeat and the behaviors of other vital organs such as the brain or even sleeping patterns, the Electro-
