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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images.

Vahid Faghih Dinevari¹, Ghader Karimian Khosroshahi¹, Mina Zolfy Lighvan¹

¹Electrical and Computer Engineering Department, University of Tabriz, Tabriz 51666 16471, Iran.

Applied Bionics and Biomechanics

|August 2, 2016

Summary

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Endoscopic Procedures III: Video Capsule Endoscopy

Endoscopic Procedures III: Video Capsule Endoscopy

Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...

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This study introduces an automated system for detecting tumors in wireless capsule endoscopy (WCE) images. The method combines discrete wavelet transform (DWT) and singular value decomposition (SVD) for accurate tumor identification, achieving high sensitivity and specificity.

Area of Science:

Medical Imaging
Gastroenterology
Computer Vision

Background:

Wireless capsule endoscopy (WCE) enables noninvasive gastrointestinal tract visualization for disease diagnosis.
The high volume of WCE images necessitates automated methods for efficient analysis.
Current diagnostic processes for WCE images are time-consuming for clinicians.

Purpose of the Study:

To develop an automated system for detecting tumors in WCE images.
To improve the efficiency and accuracy of WCE image analysis.
To reduce the diagnostic burden on healthcare professionals.

Main Methods:

Feature extraction using discrete wavelet transform (DWT) and singular value decomposition (SVD) on WCE images.
Utilizing rotation-invariant features that capture multiresolution characteristics.

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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Classification of images using a support vector machine (SVM) algorithm.

Training and testing on a dataset of 400 normal and 400 tumor WCE images.

Main Results:

The proposed algorithm demonstrated effective detection and isolation of tumor images.
Achieved a sensitivity of 94% in the RGB color space.
Reported a specificity of 93% and an accuracy of 93.5% in the RGB color space.

Conclusions:

The developed method shows significant promise for automated tumor detection in WCE.
The combination of DWT, SVD, and SVM offers a robust approach for WCE image analysis.
Automated detection systems can enhance diagnostic efficiency and accuracy in gastroenterology.