Method for identifying benign and malignant lung nodules based on multi-dimensional information

Abstract

The invention discloses a method for identifying benign and malignant lung nodules based on multi-dimensional information. The method comprises the following steps: 1, representing a three-dimensional nodule in a two-dimensional way; 2, building a feature model; 3, building a fuzzy classifier; 4, evaluating the classifying performance. The method has the beneficial effects that accurate feature modeling is crucial to the identification of benign and malignant lung nodules. More objective bases are laid for the identification of the benign and malignant lung nodules by adopting imaging diagnosis features, common shapes and textual features in image processing, and patient information, feature extraction is performed on a two-dimensional image generated on the basis of a helical scanning technology, and a novel method is adopted for feature modelling, so that the extracted features are more accurate. In the method, a fuzzy C-means (FCM) clustering algorithm is adopted for identifying benign and malignant status of a suspected nodule, and a probability indicating the suspected nodule is benign or malignant is given, so that the method is more accordant with the thinking mode of a doctor.

Description

technical field [0001] The invention relates to a method for distinguishing benign and malignant pulmonary nodules based on multidimensional information, in particular to feature modeling and the construction of a fuzzy C-means clustering classifier. technical background [0002] At present: a lot of work has been done on the differentiation of benign and malignant pulmonary nodules. In foreign countries, Suzuki et al. used a CAD system composed of six large-scale trained artificial neural networks to distinguish benign from malignant pulmonary nodules; Eight attributes of benign and malignant discrimination; Dolejei et al. proposed an adaptive enhancement algorithm with different weights for missed pulmonary nodules and misclassified tissues, which reduced the false negative rate and false positive rate; Antonelli et al. simulated a physician The team established a multi-classifier system to complete the identification of benign and malignant pulmonary nodules by voting; K...

AI Technical Summary

Problems solved by technology

However, in clinical diagnosis, sometimes the doctor cannot give the exact diagnosis result, but gives the probability that the pulmonary nodule is malignant or benign, which leads to misdiagnosis and delays the treatment of patients

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