Medical image segmentation method based on interactive foreground extraction and information entropy watershed

Abstract

The invention discloses a medical image segmentation method based on interactive foreground extraction and information entropy watershed. The medical image segmentation method comprises the followingsteps: standardizing an original image; eliminating white point noise and edges existing in the image by utilizing morphological opening operation; marking the approximate position of the foreground of the image by using a rectangular frame, and removing a background area in the image; modeling the deterministic foreground and background of the image by using a Gaussian mixture model, creating newpixel distribution, and generating a complete image; finding a threshold value of complete image segmentation by utilizing the image information entropy, and converting the threshold value into a binary image; and performing image extraction on the binarized image through a watershed algorithm to obtain a required image. Image edges are filtered through an interactive foreground extraction method, images can be effectively segmented by combining information entropy and a watershed algorithm to acquire complete liver CT images. Problems that interference caused by uneven distribution of pixelvalues, mutual connection of foreground sub-images and different shapes of liver organs among individuals are overcome.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a medical image segmentation method based on interactive foreground extraction and information entropy watershed. Background technique [0002] With the development of medical imaging technology and image pattern recognition technology, image segmentation plays a leading role in the field of medical processing and analysis. The main purpose of image segmentation is to segment medical images with specific meanings for clinical diagnosis and pathological research. Provide reliable evidence, effectively reduce the burden of mechanized film reading for doctors, and make more accurate diagnoses. Due to the complexity of medical images, a series of problems such as individual differences and uneven distribution of pixel values ​​need to be solved in the segmentation process. At present, there is no general medical image segmentation theory and method. [0003] At present, there are ma...

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Problems solved by technology

[0004] Usually in abdominal CT images, pixel values ​​are generally concentrated in a lower range, and there is a lack of contrast between abdominal organs. At the same time, due to organ lesions and differences in the shape and size of organs between individuals, the traditional method of using gray information values ​​to find image edges It is difficult to obtain a better segmentation effect with the segmentation method, and its processing has the influence of incomplete segmentation or more noise

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