A Superpixel Method for Medical Image Segmentation

Abstract

The invention provides a superpixel method for medical image segmentation. The method comprises the steps of: processing a medical image into a superpixel; for the medical images obtained after superpixel segmentation, using bilateral filtering to preserve the edge and filter the noise to reduce the error rate of the network model; configuring a network framework, and constructing a convolution network for the medical images obtained after superpixel segmentation by iterative training parameters. Based on the linear iterative clustering segmentation method, this method applies the thought of the U-Net network to the post-optimization of super-pixels, which makes up the defect of inaccurate segmentation of inner edge of super-pixel, increases the standard layer to improve the weight sensitivity of each network layer, improves the convergence performance of the network, and makes the segmentation result closer to the actual value. Because the anatomical structure and pathological tissueof medical images are very clear, the medical images segmented by SLIC algorithm can obtain more comprehensive super-pixel, and the edge accuracy of super-pixel can be further improved by convolutionnetwork.

Description

technical field

[0001] The invention relates to the field of medical images, in particular to a superpixel method for medical image segmentation. Background technique

[0002] Medical images from various imaging techniques [1] , such as ultrasound, computed tomography (CT), X-ray, and magnetic resonance imaging (MRI), are used to describe the anatomical structure of different tissues of the human body. The anatomical map depicted by medical images reflects the health of the human body, and understanding the detailed division of regions in the anatomical map of various parts of the human body is helpful for auxiliary diagnosis and next-step treatment. For example, the lung window includes the lung parenchyma, mediastinum, pleura, and diseased tumors. Accurate segmentation of tumors can help determine the condition more comprehensively and accurately, select appropriate radiotherapy methods, and improve the success rate of treatment. However, the traditional manual segmentat...

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