Multifractal Parametrization in Diagnosis of Lungs Diseases

Troshin, P. I.

doi:10.12732/ijpam.v105i2.5

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [Hernández and Córdova Fraga, 2015] box counting dimension was used, as well as other fractal features obtained using MATLAB tool for evaluating retina images. In [Troshin, 2015] multifractal spectrum was used to parameterize regions of interest within a chest X-ray and then so detect asymmetrical lesions.…”

Section: Introductionmentioning

confidence: 99%

Wavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images

Sierra-Ponce¹,

León-Mecías²,

Santiago³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%