Cancer prognosis model construction method combining global weighted LBP and texture analysis

Abstract

The invention discloses a cancer prognosis model construction method combining global weighted LBP (Local Binary Pattern) and texture analysis. The cancer prognosis model construction method comprisesthe following steps: acquiring original preoperative CT (Computed Tomography) image data of a cancer patient, marks of focus parts and survival data; calculating a three-dimensional global weighted LBP for the original CT data, and reconstructing new image data, namely global weighted LBP data; for the obtained global weighted LBP data, using GLSZM texture analysis to extract texture features. Texture analysis characteristics extracted by the method provided by the invention highlight tumor characteristics of a patient, and a prognosis model construction result is more effective; GLSZM is used for representing texture features, the effects in the aspects of texture consistency, rotation invariance and aperiodicity are remarkable, and the method has better performance than a co-occurrencematrix and a travel matrix in the aspect of cell nucleus and CT image texture.

Description

technical field [0001] The invention belongs to the technical field of computer medical image information processing, in particular to a cancer prognosis model construction method combined with global weighted LBP and texture analysis. Background technique [0002] As the most widely used imaging modality, CT is commonly used in the preoperative diagnosis of cancer patients, but due to the poor contrast resolution of the lesion, it is difficult to distinguish different tissue layers in CT. Due to the strong subjectivity of manual analysis methods, different doctors have large inconsistencies in manual scoring under the same objective conditions. In addition to being easily affected by subjective and environmental factors, manual analysis is also very time-consuming and labor-intensive, and the human cost is high. In recent years, radiomics has become a cutting-edge discipline. Among them, texture analysis has become an important visual underlying feature, and some characte...

AI Technical Summary

Problems solved by technology

Combining local binary patterns (LBP) is based on extracting local primitives or primitives and measuring their distribution through histograms. It has significant advantages such as grayscale invariance and rotation invariance, but its accuracy in texture classification is relatively low. Low, less robust to noise

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