Customizable Image Research Articles

Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer Rates and pHs.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. 'Supercascade Red', 'Wave© Purple', and 'Carpet Blue' Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for 'Carpet Blue' and 'Wave© Purple' (p > 0.05), though 'Supercascade Red' was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in 'Wave© Purple' (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

Spectral imaging based on custom-made multi-strip filter arrays

Spectral imaging technology based on on-chip spectroscopy can find applications in areas including aerospace, industrial and consumer electronics, and so on. Since each application normally requires a different set and number of spectral bands, the development of customized spectroscopy solutions with more compact size and lower cost becomes quite important. In this paper, we demonstrate a compact, highly customizable imaging spectrometer scheme based on custom-made multi-strip filter arrays, which maintains an average high transmission of ∼85%, narrow bandwidth of ∼30nm, and high optical density of ∼OD2 in the blocking regions across the visible to near-infrared waveband. Spectral imaging experiments are conducted, and the accurate reconstruction of sparse spectral image data is demonstrated as well to prove the validity of the proposed scheme. As a result, the work reported in this paper allows researchers to develop customized spectral imaging equipment in a relatively easy way and also has a great potential to be engineered further for scalable production with a quite low cost.

Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease

The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.

Text-Guided Customizable Image Synthesis and Manipulation

Due to the high flexibility and conformity to people’s usage habits, text description has been widely used in image synthesis research recently and has achieved many encouraging results. However, the text can only determine the basic content of the generated image and cannot determine the specific shape of the synthesized object, which leads to poor practicability. More importantly, the current text-to-image synthesis research cannot use new text descriptions to further modify the synthesis result. To solve these problems, this paper proposes a text-guided customizable image synthesis and manipulation method. The proposed method synthesizes the corresponding image based on the text and contour information at first. It then modifies the synthesized content based on the new text to obtain a satisfactory result. The text and contour information in the proposed method determine the specific content and object shape of the desired composite image, respectively. Aside from that, the input text, contour, and subsequent new text for content modification can be manually input, which significantly improves the artificial controllability in the image synthesis process, making the entire method superior to other methods in flexibility and practicability. Experimental results on the Caltech-UCSD Birds-200-2011 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our proposed method’s feasibility and versatility.

Customizable GAN: A Method for Image Synthesis of Human Controllable

In the research of computer vision, artificial controllability of image synthesis is a significant and challenging task. At present, there are two available methods. One is to utilize a simple contour to determine the shape of the synthetic object. This method has a promising effect, but it can only control the shape information of the synthetic object, but not the specific content. The other is to employ the text description to synthesize the corresponding image, which effectively controls the specific content of the synthesis, but it cannot do anything for the synthesized shape. In this paper, we propose a highly flexible and human customizable image synthesis model based on simple contour and natural language description, in which the specific content of contour and text description can be determined artificially. The contour determines basic synthetic object shape, and the natural language describes specific object content. Based on these, highly authentic and customizable images can be synthesized. The experiments are executed in the Caltech-UCSD Birds (CUB) and Oxford-102 flower datasets, and the experimental results demonstrate the effectiveness and superiority of our method. The results not only maintain the contour but also conform to the natural language description. Simultaneously, the high-quality image synthesis results, based on artificial hand-drawing contour and text description, are displayed to illustrate the high flexibility and customizability of our model.

Fuzzy tissue detection for real-time focal control in corneal confocal microscopy

Abstract Corneal confocal laser scanning microscopy is a promising method for in vivo investigation of cellular structures, e. g., of nerve fibers in the sub-basal nerve plexus. During recording, even slight displacements of the focal plane lead to images of adjacent tissue layers. In this work, we propose a closed-loop control of the focal plane. To detect and evaluate the visible tissues, we utilize the Bag of Visual Words approach to implement a customizable image processing pipeline for real-time applications. Furthermore, we show that the proposed model can be trained with small classification datasets and can be applied as a segmentation method. The proposed control loop, including tissue detection, is implemented in a proof-of-concept setup and shows promising results in a first evaluation with a human subject.

A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation

The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based on an industrial multi-sensor camera and a customizable image processing routine. The system was tested in a split fertilized N field trial at different growth stages in between the end of stem elongation and the end of anthesis. The acquired multispectral images were processed to normalized difference vegetation index (NDVI) and red-edge inflection point (REIP) orthoimages for an analysis with simple linear regression models. The best results for the estimation of above-ground biomass were achieved with the NDVI (R 2 = 0.72–0.85, RMSE = 12.3%–17.6%), whereas N content was estimated best with the REIP (R 2 = 0.58–0.89, RMSE = 7.6%–11.7%). Moreover, NDVI and REIP predicted grain yield at a high level of accuracy (R 2 = 0.89–0.94, RMSE = 9.0%–12.1%). Grain protein content could be predicted best with the REIP (R 2 = 0.76–0.86, RMSE = 3.6%–4.7%), with the limitation of prediction inaccuracies for N-deficient canopies.

A Portable Plug-and-Play Imaging System for Physiological Monitoring

&lt;p&gt;Non-contact physiological monitoring has garnered interest in the&lt;br /&gt;research community. However, studies employ different imaging&lt;br /&gt;system configuration, including illuminant profile and wavelength,&lt;br /&gt;camera type and frame rate, and distance to the skin. In this paper,&lt;br /&gt;we propose an easily customizable imaging system for evaluating&lt;br /&gt;physiological monitoring parameters. The system’s design allows&lt;br /&gt;plug-and-play compatibility with various illumination sources,&lt;br /&gt;camera types, lenses, and optical components. Results using one&lt;br /&gt;configuration shows the feasibility of spatial blood perfusion analysis&lt;br /&gt;using this imaging system, where areas exhibiting clean blood&lt;br /&gt;pulsatility can be identified at the surface level.&lt;/p&gt;

An architecture for implementing customizable medical image processing systems.

Monolithic image processing systems containing a superset of imaging algorithms are difficult to use and require specialized knowledge of image processing. Thus they increase the workload of medical personnel instead of making the work situation easier. Customizable medical image processing systems on the other hand may be easily adapted to address various problems in the medical image processing domain integrating only the necessary subset of image processing functionality presented in on intuitive way. In this work we present an architecture for creating customizable image processing systems for the medical domain. We address three major topics: 1.) easy, goal-oriented customization of imaging systems by using a generalized algorithm model and repository, 2.) dynamic, data-oriented parameterization of the selected algorithms and 3.) semi-automated generation of user interface components for each new algorithm to be inserted in an imaging system based on cognitive ergonomics. We conclude with the presentation of an initial implementation of the architecture in form of an object-oriented framework for the creation of components for customizable medical imaging systems.