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Imaging Studies I: Kidney, Ureter, and Bladder Studies

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

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CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.

Abror Shavkatovich Buriboev¹, Ahmadjon Khashimov², Akmal Abduvaitov³

¹Department of AI-Software, Gachon University, Seongnam-si 13120, Republic of Korea.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

A modified CLAHE preprocessing method significantly improves kidney segmentation by enhancing medical image clarity. This approach boosts Convolutional Neural Network (CNN) accuracy, leading to more reliable diagnostic results.

Keywords:

CNN image enhancement kidney segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate kidney segmentation is crucial for clinical diagnosis.
Existing preprocessing methods may not fully optimize image quality for segmentation tasks.
Convolutional Neural Networks (CNNs) show promise but are sensitive to image quality.

Purpose of the Study:

To introduce a modified CLAHE (Contrast Limited Adaptive Histogram Equalization) preprocessing technique for kidney segmentation.
To evaluate the impact of this modified CLAHE on image quality and CNN performance.
To compare the modified CLAHE method against original and standard CLAHE preprocessing.

Main Methods:

Implementation of a modified CLAHE algorithm for image preprocessing.
Quality assessment using the BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) metric.
Training and evaluation of a CNN for kidney segmentation on the KiTS19 dataset.

Main Results:

The modified CLAHE method significantly reduced the BRISQUE score from 28.8 (original) to 21.1, indicating improved image quality.
CNN segmentation accuracy increased from 0.951 (original) to 0.996 with modified CLAHE.
The modified CLAHE method outperformed standard CLAHE preprocessing, achieving an accuracy of 0.996 compared to 0.969.

Conclusions:

The modified CLAHE preprocessing technique enhances medical image quality and boosts CNN-based kidney segmentation accuracy.
Adaptive preprocessing strategies are valuable for improving medical imaging workflows.
This method offers a pathway to more accurate and dependable segmentation for clinical applications.