Pathology identification method for routine scan CT image of liver based on random forests

Abstract

The invention discloses a pathology identification method for a routine scan CT image of the liver based on the random forests. The method comprises that image gray-level texture characteristic is extracted from a pathologic area of the routine scan CT image of the liver and serves as image characteristic vector expression, the random forests is used to select characteristics from the image characteristic vector of the pathologic area of the routine scan CT image of the liver to form a most effective characteristic combination, a most effective characteristic data set is trained and learned, the identification capability of a decision tree of random forests is balanced and optimized, and a final pathology identification model is obtained.

Description

technical field [0001] The invention relates to a method for identifying pathological changes in plain scan CT images of the liver based on a random forest algorithm, in particular to the introduction of the most effective feature selection method and the improvement of the random forest algorithm. Background technique [0002] With the development and maturity of medical imaging technology, medical imaging plays an important role in the diagnosis of liver diseases. At present, liver cancer has become one of the diseases with the highest fatality rate in the world. Due to the lack of treatment methods and the lack of obvious pathological indicators of early liver cancer, it may cause misdiagnosis and miss the best time for treatment. The diagnosis of liver cancer mainly relies on liver biopsy technology, but this technology will cause certain damage to the patient's liver, coupled with high difficulty in implementation and slow postoperative recovery. Therefore, the current ...

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Problems solved by technology

At present, liver cancer has become one of the diseases with the highest fatality rate in the world. Due to the lack of treatment methods and the lack of obvious pathological indicators of early liver cancer, it may cause misdiagnosis and miss the best time for treatment.

The diagnosis of liver cancer mainly relies on liver biopsy technology, but this technology will cause certain damage to the patient's liver, coupled with the high difficulty of implementation and slow postoperative recovery. Therefore, the current diagnosis of liver diseases mainly relies on medical imaging, such as liver CT

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