Abstract
Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.
Highlights
Automatic image segmentation plays an important role in various image processing fields, which is a hot issue in the field of image processing
The superpixel segmentation algorithm has developed in the image field [6] with a smaller calculation amount, faster-running speed, stronger anti-noise, and more robust, which is widely used in image segmentation and classification in various fields [7,8]
In the Related Works section, the paper introduces the development of the superpixel segmentation algorithm; in the Materials and Methods section, the paper introduces the simple non-iterative clustering (SNIC) algorithm, the method we proposed, and the experimental environment and data set; in the Discussion section, we did comparative tests to prove The feasibility of the proposed algorithm
Summary
Automatic image segmentation plays an important role in various image processing fields, which is a hot issue in the field of image processing. Image segmentation technology is the foundation of medical image processing, whether it is disease detection and classification, or three-dimensional reconstruction of organs. In the computer-aided diagnosis system, the automatic image segmentation will replace the doctor’s manual segmentation, saving manpower and material resources, and playing an important role in the diagnosis and treatment of diseases. There have been great developments in the field of medical image segmentation. The superpixel segmentation algorithm has developed in the image field [6] with a smaller calculation amount, faster-running speed, stronger anti-noise, and more robust, which is widely used in image segmentation and classification in various fields [7,8]
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