- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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STATISTICAL DETECTION OF BREAST CANCER BY MAMMOGRAM IMAGE
摘要： Objective: To create awareness about the breast cancer which has become one of the most common diseases among women that leads to death if not recognized at early stage. Methods: The technique of acquiring breast image is called mammography and is a diagnostic and screening tool to detect cancer. A cascade algorithm based on these statistical parameters is implemented on these mammogram images to segregate normal, benign, and malignant diseases. Results: Statistical features - such as mean, median, standard deviation, perimeter, and skewness - were extracted from mammogram images to describe their intensity and nature of distribution using ImageJ. Conclusion: A noninvasive technique which includes statistical features to determine and classify normal, benign, and malignant images are identified.
关键词： ImageJ,Malignant,Mammogram image,Benign,Breast cancer
更新于2025-09-23 15:23:52
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NanoJ: a high-performance open-source super-resolution microscopy toolbox
摘要： Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJSQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.
关键词： Virus,Vaccinia,Archaea,Quantitative imaging,Sulfolobus acidocaldarius,Super-resolution microscopy,Fluidics,Modelling,Resolution,Image quality assessment,Pox,Image analysis,ImageJ,Fiji
更新于2025-09-19 17:15:36
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Precise differential diagnosis of acute bone marrow edema and hemorrhage and trabecular microfractures using na?ve and gamma correction pinhole bone scans
摘要： Objective: To analyze the performance of sequential na?ve pinhole bone scan (nPBS) and gamma correction pinhole bone scan (GCPBS), reinforced by ImageJ densitometry and pixelized micro-fracture measurement, for making specific diagnoses of bone marrow edema (BME), bone marrow hemorrhage (BMH), and trabecular microfractures (TMF). Methods: We prospectively examined BME, BMH, TMF, and normal trabeculae in 10 patients using sequential nPBS and GCPBS. The intensity of 99mtechnetium-hydroxydiphosphonate (99mTc-HDP) uptake was measured using a pixelized method and calculated using ImageJ densitometry in terms of arbitrary units (AU). This overall method was termed a visuospatial-mathematic assay (VSMA). We analyzed the ability of the calculated AU values to discriminate between the four states using GraphPad Prism software, with reference to previous morphological data. Results: The calculated values were categorized as ≤50 AU for normal trabecula, 51–100 AU for BME, 101–150 AU for BMH, and ≥151 AU for TMF. The difference in uptake between normal trabecula and BME was significant and the differences among BME, BMH, and TMF were highly significant. Conclusion: VSMA is a useful technique for refining objective individual diagnoses and for differentiating and quantitating BME, BMH, and TMF.
关键词： ImageJ densitometry,trabecular microfracture,bone marrow hemorrhage,bone marrow edema,visuospatial-mathematic assay,Bone scintigraphy
更新于2025-09-19 17:15:36
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Automated cell segmentation in FIJI? using the DRAQ5 nuclear dye
摘要： Background: Image segmentation and quantification are essential steps in quantitative cellular analysis. In this work, we present a fast, customizable, and unsupervised cell segmentation method that is based solely on Fiji (is just ImageJ)?, one of the most commonly used open-source software packages for microscopy analysis. In our method, the “leaky” fluorescence from the DNA stain DRAQ5 is used for automated nucleus detection and 2D cell segmentation. Results: Based on an evaluation with HeLa cells compared to human counting, our algorithm reached accuracy levels above 92% and sensitivity levels of 94%. 86% of the evaluated cells were segmented correctly, and the average intersection over union score of detected segmentation frames to manually segmented cells was above 0.83. Using this approach, we quantified changes in the projected cell area, circularity, and aspect ratio of THP-1 cells differentiating from monocytes to macrophages, observing significant cell growth and a transition from circular to elongated form. In a second application, we quantified changes in the projected cell area of CHO cells upon lowering the incubation temperature, a common stimulus to increase protein production in biotechnology applications, and found a stark decrease in cell area. Conclusions: Our method is straightforward and easily applicable using our staining protocol. We believe this method will help other non-image processing specialists use microscopy for quantitative image analysis.
关键词： Batch processing,Fiji,Cell segmentation,DRAQ5,Image processing,ImageJ
更新于2025-09-19 17:15:36
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SoilJ: An ImageJ Plugin for the Semiautomatic Processing of Three-Dimensional X-ray Images of Soils
摘要： Noninvasive three- and four-dimensional X-ray imaging approaches have proved to be valuable analysis tools for vadose zone research. One of the main bottlenecks for applying X-ray imaging to data sets with a large number of soil samples is the relatively large amount of time and expertise needed to extract quantitative data from the respective images. SoilJ is a plugin for the free and open imaging software ImageJ that aims at automating the corresponding processing steps for cylindrical soil columns. It includes modules for automatic column outline recognition, correction of image intensity bias, image segmentation, extraction of particulate organic matter and roots, soil surface topography detection, as well as morphology and percolation analyses. In this study, the functionality and precision of some key SoilJ features were demonstrated on five different image data sets of soils. SoilJ has proved to be useful for strongly decreasing the amount of time required for image processing of large image data sets. At the same time, it allows researchers with little experience in image processing to make use of X-ray imaging methods. The SoilJ source code is freely available and may be modified and extended at will by its users. It is intended to stimulate further community-driven development of this software.
关键词： ImageJ plugin,image processing,SoilJ,3-D X-ray imaging,vadose zone research
更新于2025-09-10 09:29:36