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- W3204268148 abstract "Image segmentation is the process of dividing an image into multiple portions and assigning a label to the portions that have similar characteristics. The pulse-coupled neural network (PCNN) models can segment the objects in an image. This paper analyzes the suitability of PCNN models for high-performance biomedical image segmentation. In this research work, three different PCNN models have used to evaluate the performance of classifying the medical images, specifically traditional PCNN, intersecting cortical model PCNN (ICM-PCNN), and unit linking PCNN (UL-PCNN). Various PCNN models were used to extract the essential features from the images and classify the images. The segmentation results obtained by different PCNN models are compared based on entropy, standard deviation, and correlation. The results of PCNN are considered the best-segmented images as it gives the proper output in lesser iteration with the highest entropy, and corresponding standard deviation and correlation are calculated." @default.
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- W3204268148 date "2021-01-01" @default.
- W3204268148 modified "2023-10-16" @default.
- W3204268148 title "Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation" @default.
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- W3204268148 doi "https://doi.org/10.1007/978-981-16-3153-5_10" @default.
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