Welcome again. This is the first video of the model of medical image analysis. In this video, the main objective is, you get an idea of the definition of digital images and also you get to know the main characteristic of these kind of images. The formal definition of a 2-D digital image is a 2-D discrete function, defined by M rows and N columns, and with values inside a dynamic range named, in this case, P. If we try to illustrate this formal definition, you can do this experiment. Just take a picture that you have gotten with your smart phone, go to the computer with this image, and use any public software to process image. When you do through many times, at the end, what you see is that a part of your image is represented by a square. Each of this square is a position in the matrix in the 2-D function that I have defined before. At the end, when we have a 2-D digital image stored in our computer, what we have is a 2-D matrix, a 2-D array. Each of the position in this array is named pixel. Pixel is the minimum information in a digital image. And this image is defined by a number of columns and a number of pixels. The generalization of these ideas to the 3D dimension is very easy. In this case, instead to talk about pixel, we talk about voxels. In this slide, you can see a sample scheme, how to obtain a digital image. What we call, in this slide, original image, it represents a scene in the work. Then, the first step is to associate small arrays in the original scene to each position with pixel in our matrix. But once we have these, there are another problem. In the real world, we have a really large number of values. In fact, as much values as the human eye can detect. So, if we want to represent this large number of digital values in a computer, where we have represented each value by a particular number of bits, we need to do a reduction from the hue range of gray levels to the restricted range of digital values in the computer. This step is called Quantization Process. So, the idea is to obtain a digital image. There are two process involved. The first one is the digital discretization process and the second one is the quantization process. When both process are finished and then, we have the matrix that we have defined in the previous slide. Well, now that you have an idea, more or less, what are the digital image is, we need to characterize or differentiate the different images. For that, we are defining different characteristics. In this slide, you have three very intuitive characteristics. The first one is the Image size that is defined by the number of pixels in the actual image. In the case of a 3D image, is the number of voxels. But in this case, through the case, the number of pixel is obtained, multiplying the number of columns by the number of rows. The second characteristic that we are talking about is Brightness. As you can see in the slide, the brightness is defined as the average light intensity. When this value is high, usually we talk about a clear, a light image. In other way, it's a dark image. A mathematical expression that we can use to obtain this value associated on an image, is as you can see in the slide, is defined by the zoom of the digital values associated to each pixel in our image, and obtaining the average value of this sum. Another characteristic is the Contrast. The contrast identifies the difference, if there are large difference between the pixels inside the particular image that we are talking about. In fact, the definition is difference in intensities in the image. As you can see in the bottom of the slides, sorry, to obtain this value, we do the difference between degree value associated to each pixel in our image with the average value of the whole image. The histogram can be defined like a characteristic of the image or like a function associated to the image, but is a very useful function. It is definition is the distribution of intensity levels. The idea is to represent in a graphic way the numbers of pixel associated to its revalue in the image. In this sense, at the top, sorry, at the bottom of this slide, you can see a particular image. In this case, it's a medical image, a histological image and on its right, you can see its histogram. As you can see, the histogram take values from 0 to 255. This means that this image is represented by eight bits because with eight bits we have two hundred fifty six different grade levels. Now we are moving to another set of characteristic. They are called, in general, Resolution, but we have different kind of resolution. We have Radiometric Resolution, Spatial Resolution, and Spectral Resolution. In this slide in particular, we are defining radiometric solution that we took a little bit in the slide before. The radiometric resolution of an image is defined by the number of bits used to represent each pixels. Sometimes, this kind of resolution is called also Image Depth. You can see in this slide the effect of decrease the number of bits used to represent a particular image. In an extreme case of use only one bit, then we have a binary image. The spatial resolution give us information about the smaller object that we can observe in a particular image. In fact, another way to interpret this characteristic is the size that the pixel represent in the real world or the area that the pixel represent in the real world. You can see also in this slide the effect to reduce the spatial resolution of a particular image. You can see that a decrease spatial resolution is the same thing, or in other word, we can say that each pixel represent a larger area in the real world. The final effect, when the spatial resolution is very small, is we have a blurred version of the original image. Finally, if we are defining the Spectral Resolution, the spectral resolution will give us information regarding the number of channels or spectral bands that a particular image has. I'm sure that all of you know what an RGV image is. It's an image in a colored space, call it RGV. RGV from red, green, and blue. In this slide, you can see the three component, the three channels of a histological image, and you can see that all of these channels are presented in gray values. However, the idea that you have of RGV image is a colored image. This is because when we have a RGV image with three different channels, what we see at the end is a composition of these three channels. Then, as you can see here, finally, we have the color composition of the image. Well, even though we are doing a very simple vision of what a 2-D digital image is and its main characteristics, I hope you get an approach idea of all these concept, and we will use it in the next video. To review, to understand better all these concept, you can consult this reference.
