Detection and Segmentation of Medical Images Using Generic Algorithms

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Author(s): Hardev Mukeshbhai Khandhar (Charotar University of Science and Technology, India), Chintan M. Bhatt (Charotar University of Science and Technology, India)and Simon Fong (University of Macau, Macau)
Copyright: 2023
Pages: 8
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch079

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

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Abstract

Image processing plays an indispensable and significant role in the development of various fields like medical imaging, astronomy, GIS, disaster management, agriculture monitoring, and so on. Medical images which are recorded in digital forms are processed by high-end computers to extract whatever information we desire. In the fast-developing modern world of medical imaging diagnosis and prognosis, where manual photo interpretation is time-consuming, automatic object detection from devices like CT-Scans and MRIs has limited potential to generate the required results. This article addresses the process of identifying Region of Interests in cancer based medical images based on combination of Otsuâ€™s algorithm and Canny edge detection methods. The primary objective of this paper is to derive meaningful and potential information from medical image in different scenarios by applying the image segementation in combination with genetic algorithms in a robust manner to detect region of interest.

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Detection and Segmentation of Medical Images Using Generic Algorithms

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