A study of hepatic fibrosis staging methods using diffraction enhanced imaging

Abstract

The early hepatic fibrosis staging is very important for timely diagnosis, prognosis, and treatment of all chronic liver diseases. Diffraction-enhanced imaging, which can provide much more information on soft tissue morphology than conventional absorption radiography, might be a potential noninvasive technique to diagnose and stage hepatic fibrosis. This paper presents different feature extraction strategies and classification methods to automatically classify hepatic fibrosis using diffractionenhanced imaging images. Texture features are obtained using a total of three methods including first order feature, gray level co-occurrence matrix, and grayscale gradient co-occurrence matrix. The fusion of these texture features is also studied. The principal component analysis is used to reduce the dimension of the features and redundant information among data. The features are classified using two popular classification techniques, namely, K-nearest neighbors and support vector machines. On the basis of the comparison of different feature strategies and classification methods, we can identify the suitable methods for grading hepatic fibrosis. The proposed approach efficiently classifies the hepatic fibrosis DEI images into four classes with the highest classification accuracy of 99.99%. We further demonstrate the potential of the DEI images in staging hepatic fibrosis.

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

“…Given that chronic fibrosis might lead to cancer, especially in the lung and liver [3,154], identifying fibrotic texture can indicate a precancerous lesion. Fibrotic tissue might be detected as a texture with increased heterogeneity and reduced correlation with neighboring pixels [155,156]. These features can be recognized by radiomics developed for texture analysis [157,158].…”

Development of a medical expert system: disease staging by a fuzzy classifier

“…According to the test results, the accuracy value for fibrosis was about 81%. Wang et al [8] present Step 1. Data for the study go through preprocessing, which includes exclusion of incomplete data, type transformation, normalization of input variables, duplicates removal, feature extraction, exclusion by inclusion criteria, and outlier test.…”