International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Trends and Techniques of Medical Image Analysis and Brain Tumor Detection Rajan Chaturvedi1 1Research scholar Department of Physics and Electronics, Dr. Rammanohar Lohia Avadh University, Ayodhya,
Uttar Pradesh, India,
Dr.R.K.Tiwari2 2Corresponding Author: Professor Department of Physics and Electronics, Dr. Rammanohar Lohia Avadh
University, Ayodhya, Uttar Pradesh, India. ------------------------------------------------------------------***---------------------------------------------------------------Abstract Image processing is adopted day by day by medical professionals for diagnostics and treatment of tumors. This application of engineering and technology is getting more and more space in the field of medical sciences. Various researchers across the world are working differently to explore the use of image processing in medical sciences and developed various algorithms and techniques to get more details like size, volume, and edges where tumors have been spread. In this paper, we have reviewed the various techniques which are carried out by different researchers in medical image analysis and brain tumor detection.
Keywords: medical image processing, brain tumor, diagnosis INTRODUCTION:
other. A major challenge is to develop appropriate validation and evaluation approaches between theoretical principles and practical utilization. Image segmentation has a vital role in image processing and the result of a proposed algorithm highly depends upon the segmentation process. There exist various preestablished segmentation techniques and their application for brain tumor detection. The development of new segmentation techniques in the last decade enhances the hidden information in medical images. A brain tumor is successfully and accurately segmented by these algorithms which help to auto-diagnose of brain tumors but for practical and real-time applications more research is required. Medical image analysis involves, image pre-processing segmentation and post-processing. For pre-processing there are various filtering techniques available, the median filter is most commonly used due to its ease and efficiency in removing salt and paper noise. Gaussian filter is useful in smoothening Gaussian noise. Sobel filter is better for edge preservation. In the segmentation process, thresholding is good for the initial stages but not useful for the extraction of much relevant information. Fuzzy C-means and K-means techniques required less human interaction and were useful in poor contrast images.
Medical image processing is a rapidly growing and challenging field for researchers. The development of medical imaging techniques like x-ray MRC, CT scan, etc. led to a great advantage to diagnose and treat various internal diseases such as Tumor, Cancer, and internal deflects in various body parts. In medical images, the diagnosis of abnormal clustering of cells and tumor highly depends upon the experience of a medical specialist. Medical image processing aims to develop a new and accurate algorithm for the automatic detection of tumors and other abnormal entities. It aims to find not only the exact location of the tumor but also its size, shape, density, type, and boundaries. The development and progress of medical image processing from 1980 up to now, achieve many milestones but still, this research field has many unresolved difficulties and challenges. The image analysis community still needs to develop new, accurate algorithms and technologies to uncover information on the Molecular and cellular levels. No researcher has fully analyzed the variability in the requisition, equipment, used algorithm, and their interaction with a human operator to characterized whole information. Over the last 20 years, separate efforts have been made by researchers to develop a new algorithm and new principle to design an appropriate model for practical uses. It needs the formation of a common database where algorithms can be compared with each
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