**1. Introduction**

Biomedical signal and digital image processing pertains to the manipulation of signal and image data to obtain output images that are useful for human health diagnostics and therapeutic purposes. This may range from the capture of a static image of the condition of an organ or tissue to the capture of multiple images at different stages of a condition to monitor the physiological process of development. It involves using different approaches for visualizing biological tissues, altogether aimed at improving health. Related research efforts lead to the development of better diagnostic tools in clinical settings as well as the improvement in developing appropriate drugs and other therapies.

Different types of techniques are used in biomedical imaging including X-rays [1], computerized tomography (CT) [2], ultrasound (U/S) [3], magnetic resonance imaging (MRI), and optical imaging. The scientific and engineering principles and technology underlying these techniques and associated sensors, instrumentation, and software continue to evolve [1, 4]. This has led to more accessibility to healthcare involving these techniques and associated technologies. [1] provided a survey of current commercial implementations and identification of essential differences

and similarities in image and signal processing approaches. The fundamentals of biomedical image processing including terminology used and imaging modalities are presented by [5]. The need to obtain high quality images of diagnostic and therapeutic relevance remains at the heart of the practice. Both high-level and lowlevel image processing and visualization are of great significance. While high-level image processing and analysis as programmed in a given biomedical device may not be directly applicable to other needs, low-level processing and analysis can be used for research and development and/or teaching purposes to explore other possibilities. Also, despite the fact that computation approaches may differ, the output images similar purposed approaches may yield similar outputs.

Researchers in medicine and biomedical science and engineering often utilize spectroscopic techniques for research and clinical purposes. Measurements in the ultra-violet, visible, and near infrared wavelength ranges are performed for different medical and biomedical applications. The use of fiber optic based spectrometers allow for in vivo measurements and testing procedures that are inexpensive and disposable. The generation of diagnostic information based on processing and analyzing digital signal and imagery obtained from such techniques has also been applied in other disciplines including agriculture [6], biology [7], and geography at different spatial scales.

Digital imagery consists of dual-dimensional arrays (rows and columns) of square pixels having values that represent signal intensities captured by the imagery sensor's detector [8]. The imagery sensor may operate in a single band (monochrome) on the electromagnetic spectrum or in multiple bands (multispectral) which may be contiguous or discrete. The pixel values of a monochrome (8-bit) image have a range of 0 to 255 (i.e., 256 colors). Multispectral image data consisting of three bands are fused to obtain color images with 16,777,216 (i.e., 256 × 256 × 256) possible colors. True color or RGB images such as are obtained by commercial digital cameras consist of data from the red (R), green (G), and blue (B) bands of the visible portion of the electromagnetic spectrum. In certain applications, different images can be layered on top of each other to give composite images containing details not found in a single image.

Several software applications have been commonly known for their use in performing low-level image processing and analysis. However, until recently, Microsoft Excel was not recognized for such utility although it had been attempted in the works of [9–11] and shown to have some potential. [9] utilized a Visual Basic for Applications (VBA) macro program for visualizing terrain image data. They determined that its function and quality were similar to a typical geographic information system (GIS). However, some macros do not work properly when used in versions of Excel other the versions used to create them. [10] also developed a clustering approach in Excel based on k-means algorithm. They made use of an Excel add-in called "loadImageArray" in acquiring RGB data from images. Likewise, [11] developed an Excel 2007 add-in known as "Image Manipulation", which can extract data (decomposed into R, G, and B data in separate workbooks) from a JPEG image, resize the image to fit the available number of columns, and export it back as JPEG images. After properly saving a required library file, the "Image Manipulation" add-in runs smoothly in later versions of Excel. It is possible to further manipulate the data using either the add-in's programmed methods or Excel's features. In view of the above examples, one of the major limitations for using Excel for image processing and analysis has been how to make the image data available in Excel 'painlessly'. Now, image data can be extracted via [12] and copied to an Excel workbook for processing and analysis.

Recently, several publications of Larbi and colleagues have extensively demonstrated the utility of Microsoft Excel for image processing and analysis. *Adopting Microsoft Excel for Biomedical Signal and Image Processing DOI: http://dx.doi.org/10.5772/intechopen.81732*

First was a study which demonstrated similarities in processed data from images captured using different cameras [13]. Image data were extracted into Excel using the RGBExcel application [14]. The RGBExcel application was further improved as a new version called RGB2X, which provides several advantages over the RGBExcel including the ability to extract data from multiple image files at once into corresponding Excel workbooks [15]. An extensive demonstration of applying the methods of image processing and analysis is presented in [16] and further expanded in [17]. The Excel based methodology was useful in demonstrating the potential use of time-lapse cameras in studying changes in condition over time [6, 18]. It was also useful in determining the optimal timing for cover crop termination to control weeds while maintaining good soybean yields [19]. Although the examples demonstrated are mainly agricultural examples, it is well understood that the methods of image processing are broadly applicable and of interdisciplinary importance.

As such, based on the potential of using Microsoft Excel for processing and analyzing digital images as shown in previous literature, the main aim of this chapter is to demonstrate the potential use in biomedical optical imaging applications. Specific illustrations of corresponding outputs of different visualization, and image enhancement, and image data fusion are provided for a better appreciation by the reader.
