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In this paper, a brain tumor segmentation method has been proposed. Image segmentation is required for the detection of brain tumors. For this purpose, brain images are divided into two distinct areas. This is one of the main but also the most complex element of the tumor identification process. A brain tumor is a result of mass of tissue that grows, it is the most important cause of the increased mortality rate among children as well as adults. This paper proposes a brain tumor segmentation method by using a combination of Adaptive Histogram Equalization and U-Net architecture. The performance of U-Net architecture and U-Net Architecture with Adaptive Histogram Equalization Technique are contrast by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), Quality and Complexity parameters. Overlap-tile strategy is used to smooth the segmentation of large images. Many MR images were collected, and the observations were carried out for statistical analysis using the Adaptive Histogram Equalization and U-Net architecture.

In medical image processing and analysis, segmentation is most compulsory assignment. In this paper, A multi-directional region growing approach is presented which use the concept of multiple seed selection to reduce the time consumption of region growing segmentation technique. The multiple seed selection concept works on the basis of eight-connected neighboring pixels. The attentiveness of the approach includes the selection of easiness of inceptive pixel and robustness to noises and the sequence of pixel execution. In order to choose a suitable threshold, the conception of neighboring difference transform (NDT) is presented for reducing the concern of threshold assortment issue. Exploratory outcome exhibits that this approach can acquire outstanding segmenting outcome; it will also not affect the result in the case of images with noises.

Speckle noise, which more commonly occurs in coherent imaging methods like as synthetic aperture radar, sonar, optical maser and ultrasound imaging, is undesirable final result of the image formation procedure in coherent imaging. In ultrasound imaging system, the presence of speckle results degradation in image quality and makes it tough for human interpretation and analysis. Consequently for examination of ultrasound images, speckle reduction is a very primary drawback. In the current era several methods for speckle suppression were proposed till now. Nevertheless in this paper, we've discussed model of speckle noise reduction with various filters that are basically used for speckle elimination. Different filters behave differently as few filters preserve the content material within the image and few filters provide smoothening process. Similarly some are better for edge detection and can be applied for tumor segmentation and so on. In the end, we have offered some regularly used filter assessment techniques.

INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interventions and surgical plans.OBJECTIVES: The objective of this paper is to design and apply an enhanced brain tumor MRI segmentation using K-mean with K-means as machine learning based Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). METHODS: A novel fitness function of Swarm Based PSO works on velocity variation is introduced, which enhances the segmented regions. The traditional k-means algorithm is enhanced by applying PSO to the segmented part. Another extension of Swarm Intelligence named Firefly is applied to compare the results of the PSO based segmentation, and Firefly based segmentation is used. RESULTS: The simulation results are evaluated in terms of precision (98%), recall (0.95), f-measure (0.96), accuracy (97%), and segmentation time (2.63s) to measure the image segmentation the quality of main results obtained.CONCLUSION: Comparative studies have shown that the proposed design using k-means combined with FA exhibited high accuracy and precision in detecting brain tumor RoI.