Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT Images

Kidney stones, common urological diseases worldwide, are formed from hard urine minerals in the kidneys. Early detection is essential to prevent kidney damage and manage recurring stones. CT imaging has made significant progress in providing detailed information for disease diagnosis.

This study aimed to enhance kidney stone detection through advanced imaging and machine learning techniques.

The Guided Bilateral Feature Detector was proposed to identify and extract features for kidney stone detection in CT images. Unlike traditional filters like Gaussian and Bilateral filters, the Guided Bilateral Filter Detector prevented halo artifacts and preserved image edges by employing a guide weight. The extracted features were combined with the SVM algorithm to accurately detect kidney stones in CT images.

The proposed detector used the Guided Bilateral Filter to reduce the halo artifacts in the images and enhance the feature detection process. The detector operated in four stages to extract important features from CT images, and a 128-feature point generator provided a more detailed representation in aiding kidney stone detection and classification. The proposed detector combined with the Support Vector Machine algorithm to improve reliability and reduce computational requirements.

Experimental results showed that the proposed Guided Bilateral Feature Detector with SVM outperformed existing models, including SIFT+SVM, SURF+SVM, PCA+KNN, EANet, Inception v3, VGG16, and Resnet50. The key performance metrics achieved included an accuracy of 98.56%, precision of 98.9%, recall of 99.2%, and an F1 score of 99%.

The findings indicate that the Guided Bilateral Feature Detector with SVM significantly enhances the accuracy and reliability of kidney stone detection, providing valuable implications for clinical practice and future research in medical imaging.