Automated dengue detection using image processing.

Dengue Detection: How Image Processing Can Help Count Platelets Faster

Rhea Morgan in Health & Wellness August 2025 • 4 min read.

"Discover how image processing techniques are revolutionizing dengue fever diagnosis by automating platelet counts, offering quicker and more cost-effective results than traditional methods."

Dengue fever remains a significant public health challenge in many developing nations, including India. Accurate and timely diagnosis is crucial for effective management and treatment, but traditional diagnostic methods can be labor-intensive and time-consuming.

This article explores how digital image processing offers a promising solution to improve dengue diagnosis. By applying segmentation techniques and morphological operations to microscopic blood smear images, we can automate the process of counting platelets, a key indicator of dengue hemorrhagic fever.

We'll delve into the methodologies, findings, and potential applications of this technology, highlighting its ability to reduce reliance on manual procedures, minimize costs, and ultimately enhance healthcare delivery in resource-limited settings.

Image Processing for Platelet Detection: A Breakdown

Automated dengue detection using image processing.

The core of this diagnostic approach lies in the application of image processing techniques to analyze blood smear images. Here’s a step-by-step overview of the process:

Image Acquisition: A digital camera attached to a traditional microscope captures a microscopic image of the blood smear. This setup is designed to be cost-efficient while providing the necessary visual data.

Image Enhancement: The captured image undergoes enhancement to improve its quality and clarity. This involves:
- Green Plane Extraction: Separating the green color plane from the RGB image, as it provides the clearest features for platelet identification.
- Contrast Adjustment: Using Contrast-Limited Adaptive Histogram Equalization (CLAHE) to adjust the contrast and brightness, making platelets more distinguishable.
Image Segmentation: This critical step involves partitioning the image to isolate the platelets. Key techniques include:
- Global Image Thresholding (Otsu's Method): Converting the image into a binary format (black and white) based on a global threshold, effectively separating platelets from the background.
- Filling of Holes: Employing morphological operations to fill any gaps within the platelets, ensuring accurate counting.
Image Labeling: Assigning unique labels to each connected region in the binary image, allowing individual platelets to be identified and counted.
Platelet Counting: Automatically counting the labeled platelets, providing a quantitative measure for diagnosis.

This automated approach offers several advantages over manual methods, including reduced labor, faster turnaround times, and improved accuracy.

The Future of Dengue Detection: Accessible, Efficient, and Accurate

The use of image processing techniques for dengue detection represents a significant step forward in making diagnosis more accessible and efficient, particularly in resource-constrained environments.

While challenges such as overlapping cells remain, ongoing research into advanced morphological operations promises to further refine the accuracy of automated platelet counting. This will not only improve the speed and reliability of dengue diagnosis but also contribute to better patient outcomes and more effective disease management.

By leveraging the power of image processing, we can transform dengue detection from a labor-intensive manual process to a streamlined, cost-effective, and highly accurate diagnostic tool, ultimately saving lives and improving public health.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.17485/ijst/2016/v9i19/93852, Alternate LINK

Title: Detection Of Dengue Fever With Platelets Count Using Image Processing Techniques

Subject: Multidisciplinary

Journal: Indian Journal of Science and Technology

Publisher: Indian Society for Education and Environment

Authors: J. Poornima, K. Krishnaveni

Published: 2016-05-27

Everything You Need To Know

How does the process of capturing a blood smear image work?

The process starts with Image Acquisition where a digital camera, connected to a traditional microscope, captures the blood smear image. This is followed by Image Enhancement, including Green Plane Extraction, where the green color plane is extracted from the RGB image for clearer features of the platelets. Furthermore, Contrast Adjustment is performed, using Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the visibility of platelets. This is crucial for accurate platelet detection.

What is the importance of image segmentation in this process?

The most critical step in the described method is Image Segmentation. This involves using techniques such as Global Image Thresholding (Otsu's Method) to convert the image to a binary format, separating platelets from the background. Morphological operations are then used to fill any gaps within the platelets. This precise segmentation is fundamental for accurate platelet identification and counting, which is key to the diagnosis of dengue fever.

What are Image Labeling and Platelet Counting, and how do they contribute to diagnosis?

Image Labeling is a process where unique labels are assigned to each distinct region identified in the binary image after segmentation. This step allows the system to distinguish individual platelets. Subsequently, Platelet Counting is performed, automatically tallying the labeled platelets. This automated counting provides a quantitative measure that is essential for diagnosing dengue hemorrhagic fever, as the number of platelets indicates the severity of the disease.

What advantages does this method offer over traditional diagnostic approaches?

Digital image processing significantly improves the diagnosis of dengue fever by automating platelet counts. This automation offers several benefits. It reduces the labor involved, shortens turnaround times for test results, and increases the accuracy of the platelet counts compared to manual methods. This leads to more accessible and efficient healthcare, particularly in areas with limited resources.

Why is this method of detecting dengue fever significant, especially in certain regions?

The use of image processing in dengue fever diagnosis has significant implications, especially in developing countries, including India, where the disease is a major public health issue. Accurate and timely diagnosis is critical for effective treatment. By automating platelet counts, this method makes diagnosis more accessible, cost-effective, and faster than traditional methods, which can lead to quicker intervention and better patient outcomes. The methodology's focus on cost-efficiency ensures its applicability in resource-limited settings.