Fuzzy rule based classification and quantification of graphite inclusions from microstructure images of cast iron.

The quantification of three classes of graphite inclusions in cast iron, namely, nodular, flake, and irregular, is the most important process in the foundry industry. This classification is based on the ISO 945 proposed morphology of graphite inclusions. This work presents a novel solution for automatic quantitative analysis of graphite inclusions into the three mentioned classes. The proposed work comprises three stages, namely, preprocessing of micrographs, classification of graphite inclusions, and then quantification of inclusions in each class. An effort has been made in this work to propose a minimum set of features to represent graphite inclusion morphology. The method employs just two geometric shape descriptors: the diameter ratio and the area ratio. A fuzzy rule based classifier is built using known feature values that are efficient in the classification of the three classes of graphite inclusions. The proposed method is automatic, fast, and provides the basis for determining many more morphological parameters that can be determined with the least effort. The results obtained by the proposed method are compared with the manual method. It is observed that the results obtained from the proposed method are useful in the optimization of cast iron manufacturing in the foundry industry.

Identification and classification of cocci bacterial cells in digital microscopic images.

In cytology, automating the feature extraction process yields an objective, quantitative, detailed and reproducible computation of cell morphofunctional characteristics and allows the analysis of a large quantity of images. The objective of the present study is to develop an automatic tool to identify and classify the different types of cocci bacterial cells in digital microscopic cell images. Geometric features are used to identify the arrangement of cocci bacterial cells, namely cocci, diplococci, streptococci, tetrad, sarcinae and staphylococci using 3σ, K-NN and Neural network classifiers. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for cocci bacterial cell classification by segmenting digital bacterial cell images and extracting geometric and statistical features for cell classification. The experimental results are compared with the manual results obtained by microbiology expert and other methods in the literature.

Oral controlled release formulations of rifampicin. Part II: Effect of formulation variables and process parameters on in vitro release.

Hydrophilic controlled release matrix tablets of rifampicin, a poorly soluble drug, have been formulated using hydroxypropyl methylcellulose (HPMC) polymer (low, medium, and high viscosity) by direct compression method. Influence of formulation variables and process parameters such as drug:HPMC ratio, viscosity grade of HPMC, drug particle size, and compression force on the formulation characters and drug release has been studied. Our results indicated that the release rate of the drug and the mechanism of release from the HPMC matrices are mainly controlled by the drug:HPMC ratio and viscosity grade of the HPMC. In general, decrease in the drug particle size decreased the drug release. Lower viscosity HPMC polymer was found to be more sensitive to the effect of compression force than the higher viscosity. The formulations were found to be stable and reproducible.