Abstract
Abstract Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.
Highlights
In the computer-aided diagnosis of human diseases from medical images, the detection of a region of interest decides the path of diagnosis
In this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing
The Matlab 2010 software was used for the analysis of typical segmentation algorithms, and the results of typical slices from medical datasets are depicted here
Summary
In the computer-aided diagnosis of human diseases from medical images, the detection of a region of interest decides the path of diagnosis. The pre-processing of the medical image was done for the removal of noise and artifacts [28, 79]. [21], selective average filter was proposed for the filtering of speckle noise. Satisfactory results were produced for synthetic ultrasound (US) images corrupted by speckle noise and real US images of female pelvic cavity. The inspiration from the behavior of herbivore organism paves the way for new artificial life model for the enhancement of synthetic and real images [20]
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