Content-based image retrieval: a new promising technique in powder technology

EFFICIENT QUATERNION MOMENTS FOR REPRESENTATION AND RETRIEVAL OF BIOMEDICAL COLOR IMAGES

Biomedical color (BMC) images are being used on a wide scale by physicians, where their diagnosis would be more accurate. Hence, it is recommended to develop new approaches that are able to represent and retrieve the BMC images efficiently. This work proposes two methods to represent BMC images: Quaternion Associated Laguerre. Moments (Q_ALMs), and Quaternion Chebyshev Moments (Q_CMs). Q_ALMs and Q_CMs are derived by extending the ALMs and CMs to the quaternion field. ALMs and CMs represent discrete orthogonal moments, and they are defined using the Associated Laguerre Polynomials (ALPs) and Chebychev Polynomials, respectively. Hospitals and medical institutes everywhere in the world create and store a large variety of datasets of BMC images during the routine clinical practices; hence, the mastery to retrieve the BMC images correctly is crucial for precise diagnoses and also for the researchers in medical sciences. So that in this study, we also introduced two image retrieval systems for BMC images based on the Q_CMs and Q_ALMs approaches. Our approaches extensively assessed with two standard benchmark datasets: LGG Segmentation dataset for brain magnetic resonance MR images and NEMA-CT for the computed tomography (CT) images. The performance of the proposed retrieval systems is assessed through three performance metrics: Average retrieval precision (ARP), average retrieval rate (ARR), and F_score. Results have shown the outperformance of Q_CMs over Q_ALMs in both the cases of representing and retrieval of BMC images.

The Future of Pharmaceutical Manufacturing Sciences

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.

On-line monitoring of fluid bed granulation by photometric imaging

This paper introduces and discusses a photometric surface imaging approach for on-line monitoring of fluid bed granulation. Five granule batches consisting of paracetamol and varying amounts of lactose and microcrystalline cellulose were manufactured with an instrumented fluid bed granulator. Photometric images and NIR spectra were continuously captured on-line and particle size information was extracted from them. Also key process parameters were recorded. The images provided direct real-time information on the growth, attrition and packing behaviour of the batches. Moreover, decreasing image brightness in the drying phase was found to indicate granule drying. The changes observed in the image data were also linked to the moisture and temperature profiles of the processes. Combined with complementary process analytical tools, photometric imaging opens up possibilities for improved real-time evaluation fluid bed granulation. Furthermore, images can give valuable insight into the behaviour of excipients or formulations during product development.

Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations

Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible.

Image analysis by pulse coupled neural networks (PCNN)—a novel approach in granule size characterization

A biologically inspired spiking neural network model, the pulse coupled neural network (PCNN), has been applied for the first time in bulk particle characterization, and specifically in the characterization of pharmaceutical granule size distributions. The PCNN was trained on surface images of pharmaceutical granule beds, and the adjustable parameters (radius neuron interconnection, r0, linking weight coefficient, β, local threshold potential, VΘ, and number of iterations) were successfully optimized using design of experiments. As demonstrated with size fractions of granules, it was found that the PCNN produced granule size-dependent signals. In general, a first highest and relatively narrow peak located in the region of two to twelve iterations corresponded to smaller particle size, while larger particles resulted in wider peaks and in highest (not first) peak at a range between 13 and 25 iterations. Better predictions, i.e. lower RMSEP (root mean squared error of prediction) values, were obtained using high β value, low r0 and VΘ values, while the number of iterations had to exceed 110 and the optimized model (RMSEP lower than 5) corresponded to PCNN variables: r0 = 1, β = 0.4, VΘ = 2, and number of iterations = 150. The coefficient of determination (R2) of the model was 0.94 and the predicted variation (Q2) was 0.91, while the Pearson correlation coefficient between the predicted and the measured mean particle size by sieving for eight test batches was 0.98. These findings could be characterized as promising and encouraging for the further use of image analysis by PCNNs in pharmaceutical bulk particle size and shape characterization.

A review of content-based image retrieval systems in medical applications-clinical benefits and future directions

Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and communication systems (PACS) have been created. This article gives an overview of available literature in the field of content-based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content-based image retrieval and the technologies used. Section 2 explains the propositions for the use of image retrieval in medical practice and the various approaches. Example systems and application areas are described. Section 3 describes the techniques used in the implemented systems, their datasets and evaluations. Section 4 identifies possible clinical benefits of image retrieval systems in clinical practice as well as in research and education. New research directions are being defined that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.

New Perspectives for Visual Characterization of Pharmaceutical Solids

The utilization of descriptive image information in pharmaceutical powder technology is rather limited. Consequently, the development of this discipline is a challenge within physical characterization of pharmaceutical solids. The aim of this study was to develop and evaluate an inventive visual characterization approach for monitoring the granule growth in a fluidized-bed granulation process and to use the generated image information in the prediction of tabletting behavior of granules. Surface images of samples from 34 granulations were continuously captured during the spraying and drying phases of the process and particle size distributions were determined. The gray scale difference matrix (GSDM) was derived from two surface images taken in controlled illumination conditions. The particle size calculation from the surface images was based on a multivariate Partial Least Square (PLS) model between the GSDM and sieve analysis measurements. The image information of the end-point samples was also evaluated with respect to tabletting behavior of the granules produced. Principal component analysis (PCA) was used for data visualization. The introduced approach was suitable in particle size measurements of granules during all process phases and in the monitoring of different kinds of granule growth behavior. The visual inspection of the granule samples was powerful, enabling representational batch-to-batch comparisons. The tabletting behavior of the granules could be predicted directly from particle size information generated from the surface images. PCA as a projection method was efficient in data visualization. Development of process analytical technologies (PAT) aims at improving the efficiency of processes. The presented visual characterization approach can be an effective process analytical tool in particle size analysis also enabling the evaluation of the further product quality in the end of the granulation process. The idea of characterization of bulk surface images opens new perspectives for characterization of pharmaceutical solids.

Characterization of particle sizes in bulk pharmaceutical solids using digital image information

The purpose of this study was to demonstrate a novel method of extracting relevant information from undispersed bulk powder surfaces to be used in particle size analysis. A new surface imaging approach for undispersed powders combined with multivariate modeling was used. Digital surface images of various granule batches were captured using an inventive optical setup in controlled illumination conditions. A descriptor, the gray scale difference matrix (GSDM), which describes the particle size of granular material was generated and extracted from the powder surface image information. Partial least squares (PLS) modeling was used to create a model between the GSDM and the particle size distribution of granules measured with sieving. The use of lateral illumination and the combining of information from 2 surface images strengthened the shading effects on the powder surfaces. The shading effects exposed the topography or the visual texture of the powder surfaces. This textural information was efficiently extracted using the GSDM descriptor. The goodness-of-fit (R2) for the created PLS model was 0.91 and the predicted variation (Q2) was 0.87, indicating a good model. The model covered granule sizes in the size range of approximately 20 to 2500 microm. The extracted descriptor was effectively used in particle size measurement. This study confirms that digital images taken from undispersed bulk powder surfaces contain substantial information needed for particle size distribution analysis. The use of the GSDM enabled the utilization of bulk powder surface information and provided a fast method for particle size measurement.

Does a powder surface contain all necessary information for particle size distribution analysis?

The aim of this study was to utilise a new approach where digital image information is used in the characterisation of particle size distributions of a large set of pharmaceutical powders. A novel optical set-up was employed to create images and calculate a stereometric parameter from the digital images of powder surfaces. Analysis was made of 40 granule batches with varying particle sizes and compositions prepared with fluidised bed granulation. The extracted digital image information was then connected to particle size using multivariate modelling. The modelled particle size distributions were compared to particle size determinations with sieve analysis and laser diffraction. The results revealed that the created models corresponded well with the particle size distributions measured with sieve analysis and laser diffraction. This study shows that digital images taken from powder surfaces contain all necessary data that is needed for particle size distribution analysis. To obtain this information from images careful consideration has to be given on the imaging conditions. In conclusion, the results of this study suggest that the new approach is a powerful means of analysis in particle size determination. The method is fast, the sample size needed is very small and the technique enables non-destructive analysis of samples. The method is suitable in the particle size range of approximately 20-1500 microm. However, further investigations with a broad range of powders have to be made to obtain information of the possibilities and limitations of the introduced method in powder characterisation.