Brain tumour segmentation in MRI using fuzzy deformable fusion model with Dolphin-SCA

It is evident that when the human brain stops functioning for a small period of time, it will lead to death. As a result, dealing with brain disorders should be done early and properly. A brain tumour is one of the most serious brain illnesses. The development of tumours can be detected using Magnetic Resonance Imaging (MRI). However, because an MRI image has loud noise, it can be hard to diagnose a tumour. The diagnosis process is slow, yet illness necessitates prompt and accurate medical attention in order for patients to survive. One of the solutions for tumour diagnosis is to employ MRI brain picture segmentation. In this designed model, MRI of the brain is collected and pre-processed with Non-Local Means (NLM) to reduce noise from captured raw data. This pre-processed image is first segmented with Region of Interest (ROI) for identifying regions of interest and then with a fusion deformable fuzzy system, which combines fuzzy C-means (FCM) and deformable systems. By analyzing the fitness value of α and β constants, segmented pictures from models are fused using the Dolphin Sine Cosine Algorithm (SCA) method to combine the model results. The integrated output from the algorithm is classified with the deep Convolutional Neural Network (CNN) classifier. The created model experimental findings are analyzed and compared to current methodologies. The proposed model performance measures are 0.90, 0.89, 0.88, and 0.10 in terms of selectivity, precision, accuracy and errors. As a result, when compared to previous strategies, the proposed approach outperforms them

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

Evaluation of automated organ segmentation for total-body PET-CT

The ability to diagnose rapidly and accurately and treat patients is substantially facilitated by medical images. Radiologists' visual assessment of medical images is crucial to their study. Segmenting images for diagnostic purposes is a crucial step in the medical imaging process. The purpose of medical image segmentation is to locate and isolate ‘Regions of Interest’ (ROI) within a medical image. Several medical uses rely on this procedure, including diagnosis, patient management, and medical study. Medical image segmentation has applications beyond just diagnosis and treatment planning. Quantitative information from medical images can be extracted by image segmentation and employed in the research of new diagnostic and treatment procedures. In addition, image segmentation is a critical procedure in several programs for image processing, including image fusion and registration. In order to construct a single, high-resolution, high-contrast image of an item or organ from several images, a process called "image registration" is used. A more complete picture of the patient's anatomy can be obtained through image fusion, which entails integrating numerous images from different modalities such as computed tomography (CT) and Magnetic resonance imaging (MRI). Once images are obtained using imaging technologies, they go through post-processing procedures before being analyzed. One of the primary and essential steps in post-processing is image segmentation, which involves dividing the images into parts and utilizing only the relevant sections for analysis. This project explores various imaging technologies and tools that can be utilized for image segmentation. Many open-source imaging tools are available for segmenting medical images across various applications. The objective of this study is to use the Jaccard index to evaluate the degree of similarity between the segmentations produced by various medical image visualization and analysis programs