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Automatic Detection of Brain Tumor using NSR Filter and K-means Clustering
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
P. Athira, Therese Yamuna Mahesh
The segmentation is a digital image processing method, which divides an image into various regions, such that the pixels within the region have similar characteristics. In the case of MRI brain image, separation of different tumor tissues from normal tissues is considered as the segmentation process. In the medical technique, segmentation of brain tumor is done manually. The manual segmentation of tumor from the images requires huge processing time and may produce inaccurate results. To help doctors for diagnosis, treatment of tumor and to help researchers for studying the brain activities, the research in automatic segmentation techniques of brain tumor are gaining more importance. Furthermore, segmentation of brain tumor is challenging task because of its unpredictable shape and appearance.
Image Processing Concepts
Published in Manas Kamal Bhuyan, Computer Vision and Image Processing, 2019
Digital image processing deals with manipulation and analysis of a digital image by a digital system. An image processing operation typically defines a new image g in terms of an input image f. As shown in Figure 2.1, we can either transform the range of f as g(x, y) = t(f(x, y)), or we can transform the domain of f as g(x, y) = f(tx(x, y), ty (x, y)). The pixel values are modified in the first transformation (Figure 2.1(a)); whereas, spatial pixel positions are changed in the second transformation (Figure 2.1(b)). The domain of an image can be changed by rotating and scaling the image as illustrated in Figure 2.2.
Introduction
Published in Qin Zhang, Roger Skjetne, Sea Ice Image Processing with MATLAB®, 2018
Digital images were first used for transferring newspaper pictures between London and New York in the early 1920s, where the pictures were coded for the submarine cable transmission and reconstructed by a special telegraph printer at the receiving end. The concept of digital image processing became meaningful and many of the digital image processing capabilities were developed in the 1960s when both hardware and software of computer technology were developed powerful enough to carry out image processing algorithms. In the 1970s, digital image processing techniques began to be used in the space program, medical imaging, remote sensing, and astronomy as cheaper and dedicated computer hardware became available. Until now, with the rapid development of computer technology, the use of digital image processing techniques has been growing by leaps and bounds, and has achieved success in many applications such as remote sensing, industrial inspection, medicine, biology, astronomy, law enforcement, defense, etc. [48].
Adaptive-sized residual fusion network-based segmentation of biomedical images
Published in Engineering Optimization, 2023
Various digital imaging technologies have been developed in recent years. Digital image processing applications include computer vision, machine interfaces, manufacturing, storage data compression and vehicle tracking. In most circumstances, digital biomedical images have different noise levels and artefacts. Noise and other undesirable errors can cause image distortion and affect the outcome. The correctness of the results is crucial in biomedical imaging. The discernibility of several details in the images may be reduced and the precision of classification may thus be affected. This work targets and improves this problem by applying an improved contrast value. Oversegmentation and heightened susceptibility to noise are two of the most significant problems associated with traditional image segmentation approaches. One solution to these issues is to use an image segmentation approach based on combining algorithms.
Energy based denoising convolutional neural network for image enhancement
Published in The Imaging Science Journal, 2023
V. Karthikeyan, E. Raja, D. Pradeep
Image processing (IP) is the process of turning an image into a digital file and then working on it to improve it or get important information from it. It is also an important area of research in engineering and computer science. The initial stage in image processing is to import images using image acquisition tools. In the following stage, analysis, editing, and image analysis are employed to generate the output of transformed images. Image processing is classified into two types: analogue and digital. Analog image processing can enhance hard copies such as prints and photographs. Image analysts apply a range of interpretive fundamentals while using these visual tools. As presented in Figure 1, ‘digital image processing’ refers to the use of computers to enhance digital images.
A Smart and Secured Approach for Children’s Health Monitoring Using Machine Learning Techniques Enhancing Data Privacy
Published in IETE Journal of Research, 2023
Labelling data for object recognition are challenging since there are several ways used to train algorithms that learn from data sets and anticipate the results. Image segmentation is a subset of digital image processing that focuses on dividing an image into distinct segments based on its characteristics and qualities. Image annotation is a way of labelling pictures including points of interest in order to make them identifiable to systems. The objective of image segmentation is to simplify or transform an image’s representation into something more relevant and easier to evaluate. It might be difficult to train an image segmentation model on new images, especially when you have to label your own data. The fundamental objective of image segmentation is to simplify the image so that it can be analysed more easily. Image segmentation is a method of breaking down a digital image into several subsets called Image segments, which serves to reduce the complexity of the image and make further processing or analysis of the image easier. In simpler terms, segmentation is the process of assigning labels to pixels. Image segmentation is commonly used to find objects and boundaries (lines, curves and so on) in images (Figure 7).