Lung CT image computer aided system and method based on cluster analysis

Abstract

The invention discloses a lung CT image computer aided system and method based on cluster analysis. The method comprises extracting the nine textural features of a lung CT image including energy, entropy, correlation, difference moment, contrast, sum average, variance, difference, and difference average based on a gray-level co-occurrence matrix; dividing samples into a training set and a verification set according to a ratio of 3:1; subjecting original high-dimensional data to dimensionality reduction by using an improved U-reliefF feature weight calculation method and calculating the corresponding weight values of respective textural features; and applying the weight values to an improved Weightedk-means algorithm to construct a nodule classification model. The method finds, by combininga plurality of textural parameters, that the combination of energy, contrast, entropy and correlation has the best classification effect, achieves a recognition rate of 81.18% of benign nodules and 91.48% of malignant nodules, has a good benign and malignant nodules recognition rate, and contributes to the early diagnosis of the lung cancer.

Description

technical field [0001] The present invention relates to the technical field of medical image analysis, in particular to a method and research for feature extraction and classification of lung CT images based on cluster analysis. Background technique [0002] Cancer (generally referring to malignant tumors) seriously threatens people's health, and lung cancer is a common malignant tumor. In China, lung cancer ranks first in terms of morbidity and mortality. In cities, lung cancer ranks first, and in rural areas it is second only to gastric cancer. Because lung cancer has no obvious symptoms in the early stage, it is already in the middle and late stage when it is discovered, and the liver tissue biopsy puncture technology currently used in the diagnosis of lung cancer will cause great harm and pain to the patient's psychology and physiology. Therefore, it is of great significance to apply computer analysis technology to intervene in treatment in order to accurately analyze a...

AI Technical Summary

Problems solved by technology

[0003] In recent years, with the continuous development of science and technology, digitalization has entered the medical field. The current era can be said to be the era of big data. However, the generation of a large amount of data makes the workload of doctors and others excessively increased, and various errors are prone to occur, such as misdiagnosis and missed diagnosis. The consequences of such a mistake in medicine are unimaginable

Images