Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review.

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.

Isoindoline Derivatives of α-Amino Acids as Cyclooxygenase 1 and 2 Inhibitors.

IC50 values were obtained for two series of isoindolines derived from α-amino acids over cyclooxygenase 1 and 2 (COX-1 and COX-2). In order to explain the biological activity observed, a structure-activity relationship (SAR) model was achieved for the tested compounds and 19 reference compounds with known selective inhibitory activity, through the correlation of the binding energies calculated from rigid docking of the best conformations into the catalytic sites of COX-1 and COX-2, as well as their molecular descriptors: Log P, molecular weight (MW), volume (V), and solvation energy (Esol) versus their experimental IC50 values by MLR and LS-SVM methods. The model probed whether the COX-1 and COX-2 inhibitory activities of the isoindolines correlate with steric, hydrophobic, and thermodynamic parameters. The correlation values with MLR for COX-1 and COX-2 (r(2) = 0.4193 and r(2) = 0.5929) were optimized with LS-SVM until r(2) = 0.6818 for COX-1 and r(2) = 0.8985 for COX-2, resulting in a good predictive ability for COX-1 and -2 inhibition with this model. In conclusion, the data suggests that the physicochemical descriptors evaluated have an impact on the inhibitory activity and selectivity of isoindolines over COX-1 and COX-2.

Sign language images dataset from Mexican sign language.

Sign language is a complete language with its own grammatical rules, akin to any spoken language used worldwide. It comprises two main components: static words and ideograms. Ideograms involve hand movements and contact with various parts of the body to convey meaning. Variations in sign language are evident across different countries, necessitating comprehensive documentation of each country's sign language. In Mexico, there is a lack of formal datasets for Mexican Sign Language (MSL), to solve this issue we structure a dataset of 249 words of the MSL divided into 17 sub-sets, we use background and clothes of black color to enhance the areas of interest (hands and face), for each word we use an average of 11 individuals, from every video sequence we obtain an average of 15 frames from each individual, obtaining 31442 jpg images.

Nucleus segmentation of white blood cells in blood smear images by modeling the pixels' intensities as a set of three Gaussian distributions.

The precise segmentation of white blood cells (WBCs) within blood smear images is a significant challenge with implications for both medical research and image processing. Of particular importance is the often neglected task of accurately segmenting WBC nuclei, an aspect that currently lacks dedicated methodologies. This paper introduces a straightforward and efficient method designed to fill this critical gap, providing an effective solution for the efficient segmentation of WBC nuclei. In blood smear imagery, the distinctive coloration of WBCs contrasts with the hues of other blood components. The inherent obscurity of WBCs prompts their segmentation by isolating pixels with minimal intensities. To streamline this process, our proposed method employs the Laplacian pyramid technique to decorrelate pixels in blood smear images, thereby amplifying the contrast. Subsequently, the intensities of pixels constituting blood cells, encompassing WBCs and the background, are modeled using three Gaussian random variables. Capitalizing on this feature, we implement the Gaussian mixture model (GMM) clustering method to determine the optimal threshold value, facilitating a highly precise segmentation of WBC nuclei. The proposed method demonstrates the capability to process images containing a single WBC as well as effectively functioning with images containing multiple cells of this type. Evaluation of the method on the ALL-IDB, ALL-IDB2, CellaVision, and JTSC datasets yielded accuracy values of 0.9802, 0.9725, 0.9772, and 0.9730, respectively. Comparative analysis with state-of-the-art methods revealed a notably comparable performance, underscoring the effectiveness of the proposed approach. The method presented in this article is highly competitive for segmenting the nuclei of WBCs compared to state-of-the-art methods. The three main advantages of our method are its ability to process images containing one or more WBCs, the automatic calculation of threshold values for each processed image, eliminating the need for manual parameter adjustments. Lastly, the method is efficient, as its algorithmic complexity is approximately O ( n m ) .

Systematic segmentation method based on PCA of image hue features for white blood cell counting.

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.