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Computer-aided diagnosis (CAD) is widely used for early diagnosis of breast cancer. The commonly used morphological feature (MF), dynamic feature (DF), and texture feature (TF) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been proved very valuable and are studied in this paper. However, previous studies ignored the prior knowledge that most of the benign lesions have clearer and smoother edges than malignant ones. Therefore, two new TFs are proposed. To obtain an optimal feature subset and an accurate classification result, feature selection is applied in this paper. Moreover, most existing CAD models with simple structure only focus on common lesions and ignore hard-to-spot lesions so that a satisfied performance can be obtained for common lesions but there are some contradictions for those hard-to-spot lesions. Therefore, in this paper, a comprehensive hierarchical model is proposed to deal with contradictions and predict all kinds of lesions. The experimental result shows that the new features obviously increase ACC of TF from 0.7788 to 0.8584 and feature selection increases ACC of DF form 0.6991 to 0.7345. More importantly, compared with the existing CAD models and deep learning method, the proposed model which provides a higher performance for both common and hard-to-spot lesions significantly increases the classification performance with sensitivity of 0.9452 and specificity of 0.9000. Graphical abstract.

Regularizing the deformation field is an important aspect in nonrigid medical image registration. By covering the template image with a triangular mesh, this paper proposes a new regularization constraint in terms of connections between mesh vertices. The connection relationship is preserved by the spring analogy method. The method is evaluated by registering cerebral magnetic resonance imaging (MRI) image data obtained from different individuals. Experimental results show that the proposed method has good deformation ability and topology-preserving ability, providing a new way to the nonrigid medical image registration.

In this paper, a computer-aided diagnostic (CAD) system for the classification of rat liver lesions from MR imaging is presented. The proposed system consists of two modules: the feature extraction and the classification modules. 40 rats are used for hepatocellular carcinoma (HCC) induction with Diethylnitrosamine via drinking water. After Resovist is administrated by tail vein the animals are scanned by a 1.5-T MR scanner with T2-weighted FRFSE sequence. SPIO-enhanced images of 106 nodules (RNs(:) 24, HCCs: 82) are acquired, and 161 regions of interest (ROIs) are taken from the MR images .Six parameters of texture characteristics including Angular Second Moment, Contrast, Correlation, Inverse Difference Moment, Entropy, and Variance of 161 ROIs are calculated and assessed by gray-level co-occurrence matrices, then fed into a BP neural network (NN) classifier to classify the liver tissue into two classes: cirrhosis and HCC. Difference of each texture parameter between cirrhosis and HCC group is significant. The accuracy of classification of HCC nodules from cirrhosis is 91.67%. It indicates the ANN classifier based on texture is effective for classifying HCC nodules from cirrhosis on rat SPIO-enhanced imaging.

Background: Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5‐grade meta‐analysis of histological data in viral hepatitis (METAVIR) staging. Purpose: This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver‐related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non‐cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary classification. Methods: We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two‐level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five‐class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of fibrosis. Results: A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(n = 16), F1(n = 28), F2(n = 29), F3(n = 44) and F4(n = 23). The performance is evaluated by five‐fold cross‐validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out. Conclusions: T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning classification. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the different pharmacokinetic parameters of the DCE-MRI method on diagnosing and staging of rabbits' liver fibrosis. Methods. We had performed DCE-MRI for rabbits that had been divided into the experiment group and the control group. Then, rabbits' images were transferred to a work station to get three parameters such as Ktrans, Kep, and Ve, which had been measured to calculate. After data were analyzed, ROC analyses were performed to assess the diagnostic performance of Ktrans, Kep, and Ve to judge liver fibrosis. Results. The distribution of the different liver fibrosis group was as follows: F1, n = 8; F2, n = 9; F3, n = 6; F4, n = 5. No fibrosis was deemed as F0, n = 6. Kep is statistically significant P < 0.05 for F0 and mild liver fibrosis stage, and the Kep shows AUC of 0.814. Three parameters are statistically significant for F0 and advanced liver fibrosis stage (Ktrans and Kep, P < 0.01 ; Ve, P < 0.05), and the Ktrans shows AUC of 0.924; the Kep shows AUC of 0.909; the Ve shows AUC of 0.848; Ktrans and Kep are statistically significant for mild and advanced liver fibrosis stages (Ktrans, P < 0.01 ; Kep, P < 0.05), and the Ktrans shows AUC of 0.840; the Kep shows AUC of 0.765. Both Ktrans and Kep are negatively correlated with the liver fibrosis stage. Ve is positively correlated with the liver fibrosis stage. Conclusion. Ktrans is shown to be the best DCE parameter to distinguish the fibrotic liver from the normal liver and mild and advanced fibrosis. On the contrary, Kep is moderate and Ve is worst. And Kep is a good DCE parameter to differentiate mild fibrosis from the normal liver. [ABSTRACT FROM AUTHOR]

Highlights • The MRI with bias field is corrected with the proposed method. • The fuzzy membership mask is used to remove the background noise. • Bias field correction can increase the accuracy of segmentation. • The proposed method has been successfully applied to the clinical liver MRI. Abstract Intensity inhomogeneity (IIH), also named as bias field, is an undesired phenomenon of liver magnetic resonance imaging (MRI) which severely affects the quantitative analysis of medical image and decreases the performance of subsequent computer aided diagnosis (CAD) of liver cirrhosis. Many algorithms have been proposed to reduce or eliminate IIH of MRI, and some notable achievements for brain MRI have been obtained. However, IIH correction of abdominal MRI receives less attention and is challenging because of the irregular structure and the wide intensity range of different tissues. In this paper, an automatic method based on the global intensity, the local intensity and the spatial continuity information is presented for reducing IIH of liver MRI. What should be noted is that the gray level should be preserved after correction since it is important for subsequent quantitative image analysis. Therefore, a constraint term is introduced based on the information of bias field intensity for appropriate IIH correction. In addition, the objective function introduces a fuzzy membership mask to remove the background noise and avoid misclassification. Our method is successfully applied to the clinical liver MRI and acquires desirable results. Compared with other approaches, our method obtains the best segmentation with Jaccard similarity (JS) = 0.88 ± 0.06, Dice index (DI) = 0.94 ± 0.03, and accuracy (ACC) = 0.99 ± 0.01. [ABSTRACT FROM AUTHOR]