**3. Image formation**

Image formation encompasses all the necessary steps from image capture to creating a digital matrix of pixels. With the purpose of using Microsoft Excel for image processing, image formation also includes making the image data available in Excel. These are discussed further in sub-sections 3.1 and 3.2.

### **3.1 Acquiring digital images**

Optical medical images can be obtained using a number of optical devices including endoscopes in their different kinds such as bronchoscopes, colonoscopes, and laparoscopes that allow physicians and clinicians to view inside various channels of anatomic tubes. Examples of such channels include the bronchial system, the gastrointestinal tract, and the genitourinary system. An extended list of the different types of endoscopes and their uses is provided by [20]. Endoscopic diagnostic procedures use a guided probe based system with a camera and lighting for imaging. More details on the procedures including pre-procedure preparation and postprocedure expectations can be accessed in [21].

Different materials reflect incident light differently. Measurements of light reflectance (proportion of incident light that is reflected) by different materials such as the human skin and internal tissues can unveil great amounts of diagnostic information about their conditions. An example of this kind of measurement is the use of a dermal auto-fluorescence measurement for detecting advanced glycogen end products (AGEs) in patients [22]. Near-infrared measurements of deep tissue reflectance is also useful for assessing the effectiveness of radiation therapy as decreasing cancerous masses. Such measurements depend on spectroscopic analysis including image processing.

Cameras or imaging systems such as discussed above output RGB or RGB-type images. For the purpose of demonstrating potential applications, image acquisition using common devices such as digital cameras and cellphones is also considered for capturing external parts of the human body to assess variability in skin condition resulting from disease or exposure to certain extreme environments. Examples of such targets include healthy skins, skins with sunburn, facial skins with acne, and skins showing different stages of a disease.

For the purpose of demonstration in this chapter, images were acquired from [23]. The scales of these images are uncertain. However, this does not diminish their usefulness for demonstration purposes. **Figure 1** shows images of tissues with various conditions: (a) eczema atopic dermatitis symptom skin; (b) allergic rash dermatitis eczema skin; (c) psoriasis vulgaris skin; and (d) colon affected by ulcerative colitis.

### **3.2 Image digitization**

Regardless of file format, image data from the R, G, and B bands can be extracted from images to Microsoft Excel using the RGB2X software application [13, 17, 24]. The extracted data are populated in different worksheets and named accordingly. **Figure 2** shows worksheet tabs corresponding to R, G, and B band data, with a section of the G band data displayed. The UC worksheet originally has three adjacent columns having sequentially unidimensional R, G, and B datasets. This allows for certain analyses like histogram and clustering to be accomplished. Alternatively, the image data can be extracted by [12] and uploaded into Excel.

*Adopting Microsoft Excel for Biomedical Signal and Image Processing DOI: http://dx.doi.org/10.5772/intechopen.81732*

#### **Figure 1.**

*Images used for demonstration: (a) eczema atopic dermatitis symptom skin; (b) allergic rash dermatitis eczema skin; (c) psoriasis vulgaris skin; and (d) colon affected by ulcerative colitis.*


**Figure 2.** *Extracted image data in Excel.*
