## 3 Region Segmentation  
Since in medical images the diseases are typically represented by local-oriented patterns surrounded and/or overlapped by the normal tissues, accurately segmenting the targeted suspicious regions is an important step in CAD development. Studies have shown that in general, the region segmentation error had substantially higher negative impact on (1) the CAD schemes that are used to classify between malignant and benign lesions than those that are applied to detect the presence of the suspicious lesions [42; 43], and (2) the CAD schemes using the CBIR approaches than the conventional CAD schemes because the adaptive or \"local\" based optimization methods used in CBIR algorithm is much more sensitive to the segmentation error than many of the \"globally\" optimized classifiers (i.e., an artificial neural network) [37]. Therefore, segmentation of images into meaningful and homogenous regions is the first key method for image analysis within CBIR applications [44]. To improve accuracy and reliability of mass region segmentation, a large number of computing algorithms have been proposed, developed, and tested in this area, which include but not limited to multi-layer topographic region growth algorithm [45; 46], active contour (snake) modeling [47; 48], adaptive region growth [49], a radial gradient index (RGI)-based modeling [50], a dynamic programming-based boundary tracing (DPBT) algorithm [51], and level set algorithm [52]. Due to the diversity of breast masses and overlap of breast tissue in the 2D projected images as well as the limited testing datasets, it is very difficult to compare the performance and robustness of these segmentation methods as well as to find out which one is always superior to the other segmentation algorithms in different image databases [11; 53]. The mass region segmentation accuracy determines whether CAD schemes enable to accurately extract and compute useful image features to detect and classify suspicious regions. For example, the spiculation level of mass boundary margin plays an important role in detecting and classifying breast masses [54]. However, since the majority of CAD schemes for detecting breast masses are applied to the sub-sampled images (i.e., sub-sampling the originally digitized image of 50 um\(\times\) 50 um pixel size by 8 folds in two dimensions to generating the lower spatial resolution image with 400 um\(\times\) 400 um pixel size), accurately segmenting the spiculated breast mass regions becomes quite difficult. A number of research groups have developed different image processing algorithms to detect spiculated masses and measure the spiculation related features [34; 55-57]. These studies typically presented or concluded the segmentation results by showing a few examples and did not report or compare the actual accuracy of detecting spiculation levels of the mass regions. Recently, one study developed another algorithm to segment mass regions and detect the region spiculation levels [58]. The unique characteristic of this study is that the researchers applied the algorithm to a publicly available image dataset with the subjective classification results provided by the radiologists and used the receiver operating characteristic (ROC) method to assess the performance of the algorithm in classifying this set of images into two groups of images depicting either spiculated or non-spiculated (circumscribed) mass regions. Although the reported classification performance was relatively lower (with the area under ROC curve = 0.701\(\pm\)0.027), it provided an example and comparison base of how to assess and report the progress in segmenting spiculated masses for the future studies. Despite of great research efforts, accurately segmenting mass regions remains a technical challenge [53] and its results substantially affects the accuracy of computed image features (i.e., mass region sizeor volume and boundary margin spiculation level).