Data Science and Intelligent Computing Techniques
Brain Tumor Detection using Segmentation and Morphological processing
Authors: Vikas h, Karamjeet Kaur, Mahesh Kumar and Yogendra Narayan
Publishing Date: 22-10-2023
ISBN: 978-81-955020-2-8
Abstract
One of the most well-known cancers, which is spreading throughout the world and steadily raising mortality rates, is the brain tumor. It has a likelihood of survival of about 75–90% when discovered in the early stages. The majority of cases are found when they are well advanced, largely because it takes so long to refer patients to brain cancer specialists and because the symptoms are not widely known. The advancement of image based machines that can be identify possible malignant high eminence brain diseases that increase the risk of emerging cancer presents substantial prospects for the advancement of brain disease screening for early recognition and behavior continue the most operative interferences in improving outcomes for brain cancer. This work proposes a segmentation and morphological approach utilising S-transform to maintain the prominent features and edge details of the MRI image. In order to maintain edge details and prominent characteristics of the MRI image, a segmentation and morphological algorithm is proposed in this study. Utilizing segmentation and morphological processing, locate the brain tumor and catch it early. This algorithm could be used to find tumors in these photos. Even when pre-processing is used on images, over-segmentation occurs. Despite being pre-processed, applying pre-processing to photos causes over-segmentation. The simulation results of cross-correlation and normalized cross correlation values is discussed in table-1.
Keywords
Brain tumor, Segmentation, DOST, Morphological processing
Cite as
Vikas h, Karamjeet Kaur, Mahesh Kumar and Yogendra Narayan, "Brain Tumor Detection using Segmentation and Morphological processing", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 509-516. https://doi.org/10.56155/978-81-955020-2-8-47