Automatic lung tumour segmentation method based on random forest and monotonically decreasing function

Abstract

The invention discloses an automatic lung tumour segmentation method based on a random forest and a monotonically decreasing function. The automatic lung tumour segmentation method comprises the following steps: obtaining the initial position of a lung tumour by utilizing the brightness variation characteristic of the tumour on a PET image, then, sufficiently utilizing metabolic information of the tumour on the PET image and anatomic information of the tumour on a CT image through characteristic extraction, and finally, realizing precision segmentation of the tumour through a random forests algorithm. The automatic lung tumour segmentation method based on the random forest and the monotonically decreasing function provided by the invention is capable of determining the position and the size of the lung tumour automatically and precisely; therefore, a doctor can be clinically assisted to treat the lung tumour; and more rapid and more precise full-automatic lung tumour segmentation can be realized without artificial intervention.

Description

technical field [0001] The present invention relates to an automatic segmentation method of lung tumors based on random forest and monotone descending function, in particular to a method for fully automatic segmentation of lung tumors by using random forest and monotone descending function, which belongs to the technical field of biomedical image processing . Background technique [0002] Tumor refers to the new organism formed by the proliferation of local tissue cells under the action of various tumorigenic factors. According to the cell characteristics of new organisms and the degree of harm to the body, tumors are divided into two categories: benign tumors and malignant tumors, and cancer is the general term for malignant tumors. [0003] Lung cancer is one of the common malignant tumors. In recent decades, the incidence and mortality of lung cancer have increased significantly. Early diagnosis of lung cancer is an effective way to improve the treatment effect. In medi...

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Problems solved by technology

However, these algorithms either use a single modality, cannot provide accurate segmentation results, or require human-computer interaction, and cannot achieve fully automatic segmentation methods. For example, algorithms based on graph cuts require manual calibration of the seed points of graph cuts.

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