OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos

Farhan, Ali; Saputra, Ferry; Suryanto, Michael Edbert; Humayun, Fahad; Pajimna, Roi Martin B.; Vasquez, Ross D.; Roldan, Marri Jmelou M.; Audira, Gilbert; Lai, Hong‐Thih; Lai, Yu‐Heng; Hsiao, Chung-Der

doi:10.3390/biology11101471

Cited by 3 publications

(3 citation statements)

References 73 publications

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“…The DICOM format data were converted to PNG without manually segmenting the backdrop to eliminate it ( 12 ); instead, the resultant pictures were processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) to ensure that they matched the original acquisition. With the help of the OpenCV ( 28 ), the CLAHE parameter, the clipLimit was 2.0, and the tileGridSize was (8, 8). The dataset was randomly partitioned into a training set (80%), a validation set (10%), and a test set (10%).…”

Section: Methodsmentioning

confidence: 99%

A real-time automated bone age assessment system based on the RUS-CHN method

Yang

Dai²,

Qin³

et al. 2023

Front. Endocrinol.

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BackgroundBone age is the age of skeletal development and is a direct indicator of physical growth and development in children. Most bone age assessment (BAA) systems use direct regression with the entire hand bone map or first segmenting the region of interest (ROI) using the clinical a priori method and then deriving the bone age based on the characteristics of the ROI, which takes more time and requires more computation.Materials and methodsKey bone grades and locations were determined using three real-time target detection models and Key Bone Search (KBS) post-processing using the RUS-CHN approach, and then the age of the bones was predicted using a Lightgbm regression model. Intersection over Union (IOU) was used to evaluate the precision of the key bone locations, while the mean absolute error (MAE), the root mean square error (RMSE), and the root mean squared percentage error (RMSPE) were used to evaluate the discrepancy between predicted and true bone age. The model was finally transformed into an Open Neural Network Exchange (ONNX) model and tested for inference speed on the GPU (RTX 3060).ResultsThe three real-time models achieved good results with an average (IOU) of no less than 0.9 in all key bones. The most accurate outcomes for the inference results utilizing KBS were a MAE of 0.35 years, a RMSE of 0.46 years, and a RMSPE of 0.11. Using the GPU RTX3060 for inference, the critical bone level and position inference time was 26 ms. The bone age inference time was 2 ms.ConclusionsWe developed an automated end-to-end BAA system that is based on real-time target detection, obtaining key bone developmental grade and location in a single pass with the aid of KBS, and using Lightgbm to obtain bone age, capable of outputting results in real-time with good accuracy and stability, and able to be used without hand-shaped segmentation. The BAA system automatically implements the entire process of the RUS-CHN method and outputs information on the location and developmental grade of the 13 key bones of the RUS-CHN method along with the bone age to assist the physician in making judgments, making full use of clinical a priori knowledge.

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Section: Methodsmentioning

confidence: 99%

A real-time automated bone age assessment system based on the RUS-CHN method

Yang

Dai²,

Qin³

et al. 2023

Front. Endocrinol.

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“…The overview of the dataset is shown in Table 1; examples of six defects in the image are shown in Figure 1. OpenCV is an open-source cross-platform computer vision and machine learnin software library [15] that is used for the comprehensive processing of images, can com plete detection and recognition tasks, and can track targets, widely used in the field o…”

Section: Datasetsmentioning

confidence: 99%

“…OpenCV is an open-source cross-platform computer vision and machine learning software library [15] that is used for the comprehensive processing of images, can complete detection and recognition tasks, and can track targets, widely used in the field of computer vision. In terms of defect detection, it can be processed according to the process in Figure 2, and these small-area defects can be extracted using morphological algorithms in OpenCV, such as corrosion, expansion, open operation, closed operation, etc.…”

Section: Related Defect Detection Algorithm 221 Opencvmentioning

confidence: 99%

A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems

Chen,

Xie

2023

Photonics

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The circuit boards in the fields of optical sensors and optical devices require extremely high levels of precision and performance. For instance, applications such as fiber optic communication, optical computing, biomedical devices, and high-performance computing devices all necessitate meticulous optical components. Any minute defect on the circuit boards of these components has the potential to adversely impact the performance of the entire device. Traditional circuit defect detection methods require manual inspection, which is very labor-intensive and time-consuming. The defect detection method based on deep learning can automatically learn features and more accurately find defects in printed circuit boards, improve detection efficiency, and reduce the workload, bringing better economic and social benefits. Based on the popular YOLOv8 model, this paper uses the open-source circuit defect dataset, introduces Wise IoU, proposes the W–YOLOv8 model, and uses the gradient gain allocation strategy of a dynamic non-monotonic focusing mechanism to make the model focus on ordinary-quality anchor boxes, which improves the performance of the original model. Experimental data show that the mAP50 of W–YOLOv8 is 97.3%, which is 1.35% higher than that of YOLOv8, and the mAP50-95 is 55.4%, which is 3.94% higher than that of YOLOv8.

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Automatic White Blood Cells Counting Using OPENCV

Telagarapu,

Chapa,

Pullanagari

2024

Advances in Medical Technologies and Clinical Practice

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Counting the number of white blood cells (WBCs) is a crucial procedure in medical laboratories for diagnosing various diseases. However, manual counting can be time-consuming and susceptible to errors. To overcome this, a research study has proposed an automated approach for WBC counting in sampled images using OpenCV, an open-source computer vision library. The authors developed an algorithm that segments the WBCs from the background by utilizing preprocessing techniques, followed by edge detection (canny edge detection) to identify the cells' boundaries. The number of cells is counted by implementing a simple circular Hough transform method. For this, the authors approached and collected datasets from ALL-IDB team for sampled images to test the proposed method. The proposed method has achieved high accuracy rates and outperformed manual counting in terms of speed and efficiency. The developed approach has the potential to be integrated into existing medical laboratory workflows, automating the WBC counting process and improving the diagnosis and treatment of various diseases.

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OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos

Cited by 3 publications

References 73 publications

A real-time automated bone age assessment system based on the RUS-CHN method

A real-time automated bone age assessment system based on the RUS-CHN method

A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems

Automatic White Blood Cells Counting Using OPENCV

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