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Area Computation by the Alternative Coordinate Method01:24

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Blood studies are critical in the medical field, enabling healthcare professionals to assess a patient's health status accurately. This page will focus on two significant blood studies: Arterial Blood Gas (ABG) and Venous Blood Gas (VBG).
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
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Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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Related Experiment Video

Updated: Jan 25, 2026

Computational Analysis of the Caenorhabditis elegans Germline to Study the Distribution of Nuclei, Proteins, and the Cytoskeleton
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Recent computational methods for white blood cell nuclei segmentation: A comparative study.

Alan R Andrade1, Luis H S Vogado1, Rodrigo de M S Veras1

  • 1Federal University of Piauí, Teresina, Brazil.

Computer Methods and Programs in Biomedicine
|May 4, 2019
PubMed
Summary
This summary is machine-generated.

This study evaluated 15 computational methods for segmenting leukocytes in blood cell images. While top methods show high accuracy, no single algorithm perfectly identifies all leukocytes, indicating ongoing challenges in automated leukemia diagnosis.

Keywords:
Clustering algorithmsLeukocyteMathematical morphologyOtsu thresholdSegmentation

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Area of Science:

  • Medical image analysis
  • Computational pathology
  • Hematology

Background:

  • Leukemia diagnosis relies on manual blood smear examination, which is subjective and prone to errors.
  • Automated methods for leukocyte segmentation are needed to improve diagnostic accuracy and efficiency.

Purpose of the Study:

  • To empirically investigate and evaluate computational methods for leukocyte segmentation.
  • To identify the strengths and weaknesses of various segmentation algorithms.

Main Methods:

  • Evaluated 15 segmentation methods across five public image databases.
  • Performed pixel-level segmentation comparison against ground truth.
  • Utilized six evaluation metrics: accuracy, specificity, precision, recall, kappa, and Dice index.

Main Results:

  • Segmentation algorithm performance varied significantly across different image databases.
  • Top-performing methods achieved >97% average accuracy and high kappa index.
  • The best method segmented only 58.44% of all leukocytes, highlighting segmentation challenges.

Conclusions:

  • Clustering algorithms, Otsu threshold, arithmetic operations, and region growing are common leukocyte segmentation approaches.
  • Despite advancements, computational methods still face challenges in fully addressing leukocyte segmentation complexities.