intensity value. It is an iterated process until there are no change in two successive iterative stages. Other criteria can be chosen; the main goal is to classify the similarity of the image into regions. Important issues Suitable selection of seed points The selection of seed points is depending on the users. For example, in a grayscale lightning image, we may want to segment the lightning from the background. Then probably, we can examine the histogram and choose the seed points from the highest range of it. More information of the image is better Obviously, the connectivity or pixel adjacent information is helpful for us to determine the threshold and seed points. Minimum area threshold No region in region growing method result will be smaller than this threshold in the segmented image. Similarity threshold value If the difference of pixel-value or the difference value of average grayscale of a set of pixels less than “Similarity threshold value”, the regions will be considered as a same region. The criteria of similarities or so called homogeneity we choose are also important. It usually depends on the original image and the segmentation result we want. Some criteria often used are grayscale (average intensity or variance), color, and texture or shape. Advantages and disadvantages Advantages Can correctly separate the regions that have the same properties we define. Can provide the original images which have clear edges with good segmentation results. Simple concept: only need a small number of seed points to represent the property we want, then grow the region. Can determine the seed points and the criteria we want to make. Can choose the multiple criteria at the same time. Theoretical very efficient due to visiting each pixel by a limited bound of times. Disadvantages Unless image has had a threshold function applied, a continuous path of points related to color may exist, which connects any two points in the image. Practically random memory access slows down the algorithm, so adaption might be needed See also k-means clustering Watershed (image processing) Notes References Jian-Jiun Ding, The class of "Time-Frequency Analysis and Wavelet Transform", the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2007. Jian-Jiun Ding, The class of "Advanced Digital Signal Processing", the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2008. W. K. Pratt, Digital Image Processing 4th Edition, John Wiley & Sons, Inc., Los Altos, California, 2007