Cirrhosis Classification Based on Texture Classification of Random Features

Abstract

Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage). CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM.

Dynamic programming and automated segmentation of optical coherence tomography images of the neonatal subglottis: enabling efficient diagnostics to manage subglottic stenosis

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Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment

“…We extracted the following four second-order statistics texture features: (1) contrast; (2) correlation; (3) energy; and (4) local homogeneity from the grey-level cooccurrence matrix (GLCM), which is the matrix M , as shown below (the measures are calculated for d = 0°). Haralick's GLCM [ 31 ] has been used successfully for biomedical image classification [ 32 , 33 ]. We used the cooccurrence matrix for the entire L intensity.…”

Hepatic cirrhosis according to the forensic medical autopsy data from Moscow for the period of 2007-2016

“…All cases of hepatic cirrhosis diagnosed during forensic medical examination of corpses were grouped by the calendar year and the two consecutive five-year periods, depending on the established morphological form of the disease. According to the pathomorphological classification based on the diameter of the connective tissue nodes, there are three main forms of hepatic cirrhosis [6] as follows: 1) micronodular form of hepatic cirrhosis (MNHC), characterised by a diameter of connective tissue nodes of less than 3 millimeters (most nodes are usually in range of 1-3 mm); 2) coarse nodular form of hepatic cirrhosis (CNHC) with a diameter of nodes of more than 3 mm; 3) mixed hepatic cirrhosis (MHC), in which there are both small and large connective tissue nodes in the liver parenchyma.…”