Method and apparatus for lung segmentation in medical image

Abstract

The invention discloses a method and an apparatus for lung segmentation in a medical image. The method comprises the following steps of obtaining an average lung template according to lung contours of M chest films in a training set, wherein M is an integer greater than or equal to 2; preprocessing a lung image to be segmented to obtain a preprocessed lung image; extracting a binary image of a lung boundary of the preprocessed lung image, and performing initial locating according to generalized Hough transform to obtain a corresponding Hough locating position; aligning the average lung template to the Hough locating position to obtain an alignment result; performing segmentation by utilizing a dynamic planning algorithm, and inversely transforming a segmentation result to be in an original coordinate system, thereby finishing lung region segmentation. According to the method and the apparatus, the lung region to be segmented is located through the Hough transform, and the locating position is aligned through an average lung region shape to obtain an initial contour of the lung, so that a deviation of the initial position and the actual position of the lung is reduced to the maximum extent and the accuracy of segmentation is improved.

Description

[0001] 【Technical field】 [0002] The invention relates to the field of medical image segmentation, in particular to a method and device for lung segmentation in medical images. [0003] 【Background technique】 [0004] Lung segmentation is an important link in digital chest radiograph image processing. The quality of the segmentation result directly affects the subsequent detection and analysis of the lesion. The biggest drawback of chest radiographs is tissue overlap. The higher the density of the tissues in the human body structure, the stronger the ability to absorb X-rays. Therefore, the ribs in the chest absorb more X-rays and appear as white shadows in the image; because the lungs contain air, they are less dense and absorb X-rays. Less, so dark shadows appear on the image. Because of this, jagged segmentation results are prone to appear when segmenting lung boundaries. Moreover, by observing the image, it can be found that there are corner points at the lower boundar...

AI Technical Summary

Problems solved by technology

But this method is to find the boundary on two-dimensional information, which is easily affected by the rib boundary, resulting in a jagged segmentation result

Images