## 2 State of the Art of DNA Microarray Image Compression
### Preprocessing
#### 2.1.2 Segmentation  
Segmentation, also know as _spot finding_ or _addressing_, consists in determining which of the image pixels belong to spots (_i.e._, the foreground), as opposed to those that do not (_i.e._, the background). As will be discussed in Section 2.4, this can be very useful in later stages of the compression process. For example, it is possible to code separately foreground and background or to exploit differences in pixel intensity distributions between sets. We discuss here only specific approaches oriented towards image compression. However, for the sake of completeness, some segmentation techniques not directly focused on compression and some general segmentation quality metrics are described at the bottom of this subsection. In 2003, [PERSON] _et al_. proposed a lossless coder whose segmentation stage consists of two steps [12]. First, spot regions are located by studying the period of the signal obtained from summing the intensity by rows and by columns, and studying its minima. After that, spot centers are estimated based on the region centroid, which is needed for their spiral scanning procedure, as explained in Section 2.4. Simpler versions of this spot region location idea had already been used in microarray image analysis and also in [PERSON] and [PERSON]'s work of 2002 [13], where they proposed a lossy-to-lossless compression scheme. In their technique, a _seeded region growing_ algorithm is used to obtain a coarse mask for the spot pixels, which is then refined. Later, in 2004, [PERSON] and [PERSON] presented their MicroZip lossy or lossless compression software [14]. They used a variation of [PERSON]'s spot region finding idea, but they considered the existence of subgrids which are located before spot regions. Four subgrids can be appreciated in Figure 1, but other images may contain more. In 2004, [PERSON] _et al._ proposed a lossy or lossless scheme [15] with a segmentation technique based on the Mann-Whitney U test, which helps in deciding whether two independent sets of samples have equally large values. Once a region containing a spot has been located, a default mask is applied to obtain an initial set of pixels classified as spot pixels, and then the test is applied to add or remove pixels from this mask. This had already been used in microarray image segmentation [16], but the authors proposed a variation that speeds up the algorithm by up to 50 times. Also in 2006, [PERSON] _et al_. described a purely lossless compression scheme [17] with a simple threshold method for dividing microarray images into low and high intensities. It consists of determining the lowest of the threshold values from \(2^{8}\), \(2^{9}\), \(2^{10}\) or \(2^{11}\) such that approximately \(90\%\) of the pixels fall within it. In 2007, [PERSON] _et al_. proposed another threshold-based technique for segmentation [18], this time in three subsets: background, edge and spot pixels. Their lossless proposal performs segmentation in two steps. First, they determine the optimal threshold value by minimizing the total standard deviation of pixels above and below it. Then they segment the image in the mentioned subsets. To do so, first they determine the spot pixels by eroding the mask formed with pixels above the selected threshold. Edge pixels are the ones surrounding the spot pixels, and background pixels are all the others. Finally in 2009, [PERSON] and [PERSON] published an approach [19] based on Cellular Neural Networks (CNNs). They define two layers for their lossless system, each with as many cells as the image has pixels. The input and state of the first layer are the pixels of the original image. Its output is the input and state of the second layer. By defining the cloning templates that drive the whole CNN dynamic, the second layer tends to its saturation equilibrium state and the resulting output tends to a \"local binary image\" where spot pixels tend to 1 and background pixels to 0.