In order to improve the diagnostic effect of MRI images, a multiparametric magnetic resonance imaging (MRI) based classification method was proposed in this paper. The study included 85 patients. The radiomics method was used to extract morphological and texture features, while Apparent diffusion coefficient (ADC) was used as functional feature.Three classification methods, including Linear Discriminate Analysis (LDA), Support Vector Machine (SVM) and Random Forest (RF), were used to distinguish benign and malignant of pulmonary lesions. The performance of multiparametric MRI sequences and single sequences were compared. The experimental results shown that multiparametric MRI classification with SVM classifier had best performence (AUC=0.82±0.03), indicating that multiparametric MR diagnosis has great potential.
Breast cancer occurs with high frequency among women. In most cases, the main early signs appear as mass and
calcification. Distinguishing masses from normal tissues is still a challenging work as mass varies with shapes, margins
and sizes. In this paper, a novel method for mass detection in mammograms was presented. First, morphology operators
are employed to locate mass candidates. Then anisotropic diffusion was applied to make mass region display better
multiple concentric layers (MCL). Finally an extended concentric morphology model (ECMM) criterion combining
MCL criterion and template matching was proposed to detect masses. This method was examined on 170 images from
Digital Database for Screening Mammography (DDSM) database. The detection rate is 93.92% at 1.88 false positives
per image (FPs/I), demonstrating the effectiveness of the proposed method.
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