###### Abstract
This database was formed in well controlled conditions, producing homogeneous images across every study, and is ideal to test automatic karyotyping systems. Nonetheless, the Q-banding technique is not very common nowadays because of the high cost of the required staining materials and equipment, as well as the lighting requirement to avoid fast vanishing of the staining effects [27]. After Q-banding, the G-banding (Giemsa stain) technique emerged, and it is currently popular because the required equipment is less costly compared to the Q-banding, preserving the stain over the same regions of the chromosomes. Even when, in the literature, the Q-banding has been used extensively for proposing automatic karyotyping systems [8; 14; 28; 29], G-banding is the staining technique used in the Children's Hospital of Tamaulipas (CHT) because of its low cost and high availability. Nowadays, there are no public databases of karyotypes images or systems to automatically build karyograms using this technique. The computer-based automatic construction of karyograms performs two complementary and sequential tasks: image segmentation and segmented chromosomes classification [30; 31]. There are several works that segment and classify Q-band chromosomes images with varying results [8; 26; 32; 33]. It is important to define the optimal chromosome features to obtain a good accuracy. In the literature, shape description, length, centromere position, and the banding pattern have been used as chromosomes descriptors [12; 18; 34; 35; 36; 31]. In Reference [8; 33], the authors annotate that these characteristics are useful to determine if the chromosome was correctly segmented, but they can not be used as the only descriptors of the chromosome. They also propose to use the band pattern profile and the intensity levels along the medial axis of the chromosome. In Reference [33], the authors approximate the medial axis using transversal lines along the chromosome with different orientations. In a similar way, in Reference [26], the medial axis is computed using transversal lines along the skeleton of the chromosome. They begin by selecting a point on one of the ends of the chromosome; then, transversal lines are traced at increasing angles until the full circle is covered. The line with the shorter length is selected and its midpoint is identified as the first coordinate of the medial axis. This process is repeated until the second endpoint is reached. Finally, the obtained coordinates are smoothed using splines. Through the analysis of the medial axis it is possible to obtain the length of the chromosome, and some other features, as the gray level intensities along this axis, and its corresponding band profile [18; 34]. In Reference [8], a feature vector is constructed using the chromosome length, the gray intensity levels along 98 transversal lines traced over the medial axis, and the chromosome area, computed by counting the active pixels of the binary chromosome image. The methodology presented in Reference [26] uses a vector composed of 131 features, including area, length, perimeter and 64 gray level intensities extracted over normal lines to the medial axis of the chromosome. These vectors are normalized, making possible to compare chromosomes from different images and improving the performance of the classifier. Reference [36] proposes the use of features inspired by a human expert's classification method such as width, position and the average intensity of the two most eye-catching regions of each chromosome to improve the classification accurate. Concerning the automatic classification of chromosomes to build a karyogram, in Reference [37], the authors propose the use of three classification techniques--(a) a backpropagation neural network, (b) fuzzy-logic rules and, (c) euclidean-distance-based template matching, reporting an Mean Squared Error accuracy of 94%, 93% and 95%, respectively, using 13 intensity values extracted along the medial axis.