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• Single/interacting drop drying patterns have been studied with gelatin and sodium sulfate. • Crystal growth depends on drop drying mechanism for single/interacting cases. • Particle size separation could be modified by the presence of another drop. • Regular crystal growth could be characterized by fractals and first/second order statistics. • Hypothesis testing shows the most significant FESEM features for these patterns. A simple colloidal drop generally forms ring like patterns after drying. The deposition morphology of the dried drop changes significantly when such a drop dries in the vicinity of another similar drop. Here we present an observational study and statistical analysis of the patterns formed inside an isolated as well as interacting drops of gelatin containing sodium sulfate (Na 2 SO 4). In all the cases, multiple concentric regions of solute particles combined with the polymer gel appear as the drops dry up. Needle crystals of sulfur and coacervates of salt and gelatin are visible in some regions. The outer region becomes non-uniform, so does the size distribution of the needle crystals and coacervates. The non-uniformity increases with proximity of the drops. Here we propose a novel mechanism of growing patterns inside the single drop during drying and correlate that with the results obtained for interacting drops. This study and the proposed mechanism provide insights into the future studies of drying drops under different physical conditions. Further we explore the statistical characteristics of the single and interacting drops using the field emission scanning electron microscopy (FESEM) images. Next, we report fractal and image texture analyses along with object shape statistics of the drop FESEM images, under various experimental conditions. Several statistical hypothesis tests have been carried out to identify the most significant features. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The aim of this study was to investigate whether using a fractal dimension as an objective index (quantitative measure) to assess and control the “visual” or “texture” similarity of reference-image regions selected by a content-based image retrieval (CBIR) scheme would (or would not) affect the performance of the scheme in classification between image regions depicting suspicious breast masses. Materials and Methods: An image data set depicting 1500 verified mass regions and 1500 false-positive mass regions was used. Fourteen morphologic and intensity distribution features and a fractal dimension were computed. A CBIR scheme using a k-nearest neighbor classifier was applied, and two experiments were conducted. In the first experiment, the CBIR scheme was evaluated using all 15 features. In the second experiment, the fractal dimension was used as a prescreening feature to guide the CBIR scheme to search for the most similar reference images that had similar measures in the fractal dimension. Results: The CBIR scheme achieved classification performance with areas under the receiver-operating characteristic curve of 0.857 (95% confidence interval [CI], 0.844–0.870) using 14 features and 0.866 (95% CI, 0.853–0.879) after adding the fractal dimension (P = .005 for both results). After using the fractal dimension as a prescreening feature, the CBIR scheme achieved an area under the receiver-operating characteristic curve of 0.851 (95% CI, 0.837–0.864), without a significant difference from the previous result using the original 14 features (P = .120). The difference of fractal dimension values between the selected similar reference images was reduced by 56.7%, indicating improvement in image texture similarity. In addition, more than half of references were discarded early, without similarity comparisons, indicating improvement in searching efficiency. Conclusions: This study demonstrated the feasibility of applying a fractal dimension as an objective (quantitative) and efficient search index to assess and maintain the texture similarity of reference mass regions selected by a CBIR scheme without reducing the scheme''s performance in classifying suspicious breast masses. [Copyright &y& Elsevier]