Wavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images
This work addresses lung nodule classification for medical imaging, but it is incremental as it builds on existing multifractal and texture feature methods.
The paper tackled lung nodule classification in X-ray images by developing a method using multifractal features from wavelet leader formalism combined with classical texture features, achieving a maximum ROC AUC of 75% and improved efficiency over prior techniques.
This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images. The proposed method includes a pre-processing step where two enhancement techniques are applied: histogram equalization and a combination of wavelet decomposition and morphological operations. As a novelty, multifractal features using wavelet leader based formalism are used with Support Vector Machine classifier; other classical texture features were also included. Best results were obtained when using multifractal features in combination with classical texture features, with a maximum ROC AUC of 75\%. The results show improvements when using data augmentation technique, and parameter optimization. The proposed method proved to be more efficient and accurate than Modulus Maxima Wavelet Formalism in both computational cost and accuracy when compared in a similar experimental set up.