A Retrieval Method for Digital Pathology Whole Slice Image

Abstract

The invention discloses a digital pathological full-slice image retrieval method, which is applied to a digital pathological full-slice image database. The method includes: extracting the positions of discrete SIFT feature points and SIFT feature vectors from the digital pathological full-slice image in the database; using LDA The model obtains the high-level semantic feature value of each SIFT feature point; the candidate area is selected by the overlapping sliding window method, and the semantic feature value of all SIFT feature points in each candidate area is counted to obtain the semantic representation vector of the candidate area; Treat the query image as a region, use the same method to get the semantic representation vector of the query image, calculate the cosine distance between the semantic representation vector of the query image and the semantic representation vectors of all candidate regions and sort these distances, and return the few with the smallest distance area. The invention can provide diagnosis reference information for pathologists, and can be used in a digital pathological full slice image database management query system and computer-aided diagnosis.

Description

technical field [0001] A digital pathology full-slice image retrieval method belongs to the field of digital image processing and machine learning, and particularly relates to digital image processing technologies such as scale-invariant feature transform (SIFT), as well as content-based image retrieval, latent Machine learning techniques such as Latent Dirichlet Allocation (LDA). Background technique [0002] Digital pathological full slide image (hereinafter referred to as full slide) is a large-size, high-resolution digital image obtained by scanning and collecting traditional glass pathological slides through a fully automatic microscope or an optical magnification system. It is an important basis for pathologists in diagnosis. In recent years, with the development of pathology and computer technology, the number of digital pathological full-slice images has grown rapidly. Finding a full-slice region similar to an undiagnosed small-size pathological image from the full-s...

AI Technical Summary

Problems solved by technology

[0004] There are currently two types of methods for image representation: 1. Directly use the underlying features to represent images, but the underlying features are very different from human understanding of images, which cannot accurately summarize image content and affect image retrieval accuracy; 2. Semantic model representation Image, semantic model is generally a summary of the underlying features, which is more in line with human thinking and improves the accuracy of image retrieval

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