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Chromatographic Methods: Classification

Chromatographic Methods: Classification

Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Computer-based classification of chromoendoscopy images using homogeneous texture descriptors.

Hussam Ali¹, Muhammad Sharif¹, Mussarat Yasmin¹

¹Department of Computer Science, COMSATS Institute of Information Technology, Wah Cantt, Pakistan.

Computers in Biology and Medicine

|July 13, 2017

Summary

This summary is machine-generated.

This study introduces Gabor wavelet texture descriptors for detecting gastric abnormalities in chromoendoscopy (CH) images. Optimized using a genetic algorithm, these features achieve 90% accuracy with SVM classification for improved computer-aided diagnosis.

Keywords:

Chromoendoscopy Classification Endoscopy Feature extraction Gastric cancer Gastrointestinal Genetic algorithm Homogeneous texture Local binary patterns

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Gastroenterology

Background:

Detecting gastric abnormalities from endoscopic images is challenging due to variations in illumination and field of view.
Robust features are needed for computer-assisted diagnostic systems to overcome these image acquisition dynamics.

Purpose of the Study:

To design Gabor wavelet-based texture descriptors for robust detection of gastric abnormalities.
To optimize feature selection using a genetic algorithm for enhanced classification accuracy.
To compare the performance of proposed features against existing texture descriptors.

Main Methods:

Texture descriptors were generated using Gabor wavelets on chromoendoscopy (CH) frames.
A genetic algorithm was employed to select an optimal subset of these descriptors.
Classifiers including SVM, Naive Bayes, k-NN, LDA, and ensemble trees were trained and evaluated.

Main Results:

The optimized Gabor wavelet features achieved 90.0% average accuracy and 0.93 AUC with the SVM classifier.
Performance was evaluated using accuracy, sensitivity, specificity, and AUC.
The proposed method demonstrated superior performance compared to other texture descriptors.

Conclusions:

Gabor wavelet texture descriptors, optimized by a genetic algorithm, are effective for classifying normal and abnormal gastric CH images.
The SVM classifier, combined with these optimized features, provides a highly accurate computer-aided diagnostic tool for gastric pathology detection.