Level set medical image segmentation aided by cooperative quantum particle optimization with Lévy flights

Image segmentation plays an important part of image processing, and is also the premise and basis of image analysis and image understanding and recognition. Among the level set based methods, the original Local Binary Fitting (LBF) algorithm is a successful deterministic algorithm that suffers from sensitization to size of the local minimum, image contours, shapes, and initial positions. Among them, Level Set method promotes the two-dimensional problem to the three-dimensional one and then solves it using implicit method to express closed curve of plane. In this article, a novel Level Set model aided by PSO was proposed to solve automated medical image segmentation. The experimental result of segmentations on the benchmark shows that our proposed method is effective to both simple and complex medical images

Local Binary Fitting Segmentation by Cooperative Quantum Particle Optimization

Recently, sophisticated segmentation techniques, such as level set method, which using valid numerical calculation methods to process the evolution of the curve by solving linear or nonlinear elliptic equations to divide the image availably, has become being more popular and effective. In Local Binary Fitting (LBF) algorithm, a simple contour is initialized in an image and then the steepest-descent algorithm is employed to constrain it to minimize the fitting energy functional. Hence, the initial position of the contour is difficult or impossible to be well chosen for the final performance. To overcoming this drawback, this work treats the energy fitting problem as a meta-heuristic optimization algorithm and imports a varietal particle swarm optimization (PSO) method into the inner optimization process. The experimental results of segmentations on medical images show that the proposed method is not only effective to both simple and complex medical images with adequate stochastic effects, but also shows the accuracy and high efficiency

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

수치 모델과 그래프 이론을 이용한 향상된 영상 분할 연구 -폐 영상에 응용-

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2016. 2. 김희찬.This dissertation presents a thoracic cavity segmentation algorithm and a method of pulmonary artery and vein decomposition from volumetric chest CT, and evaluates their performances. The main contribution of this research is to develop an automated algorithm for segmentation of the clinically meaningful organ. Although there are several methods to improve the organ segmentation accuracy such as the morphological method based on threshold algorithm or the object selection method based on the connectivity information our novel algorithm uses numerical algorithms and graph theory which came from the computer engineering field. This dissertation presents a new method through the following two examples and evaluates the results of the method. The first study aimed at the thoracic cavity segmentation. The thoracic cavity is the organ enclosed by the thoracic wall and the diaphragm surface. The thoracic wall has no clear boundary. Moreover since the diaphragm is the thin surface, this organ might have lost parts of its surface in the chest CT. As the previous researches, a method which found the mediastinum on the 2D axial view was reported, and a thoracic wall extraction method and several diaphragm segmentation methods were also informed independently. But the thoracic cavity volume segmentation method was proposed in this thesis for the first time. In terms of thoracic cavity volumetry, the mean±SD volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), and false negative ratio on VOR (FNRV) of the proposed method were 98.17±0.84%, 0.49±0.23%, and 1.34±0.83%, respectively. The proposed semi-automatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful for clinical purposes. The second study proposed a method to decompose the pulmonary vessel into vessel subtrees for separation of the artery and vein. The volume images of the separated artery and vein could be used for a simulation support data in the lung cancer. Although a clinician could perform the separation in his imagination, and separate the vessel into the artery and vein in the manual, an automatic separation method is the better method than other methods. In the previous semi-automatic method, root marking of 30 to 40 points was needed while tracing vessels under 2D slice view, and this procedure needed approximately an hour and a half. After optimization of the feature value set, the accuracy of the arterial and venous decomposition was 89.71 ± 3.76% in comparison with the gold standard. This framework could be clinically useful for studies on the effects of the pulmonary arteries and veins on lung diseases.Chapter 1 General Introduction 2 1.1 Image Informatics using Open Source 3 1.2 History of the segmentation algorithm 5 1.3 Goal of Thesis Work 8 Chapter 2 Thoracic cavity segmentation algorithm using multi-organ extraction and surface fitting in volumetric CT 10 2.1 Introduction 11 2.2 Related Studies 13 2.3 The Proposed Thoracic Cavity Segmentation Method 16 2.4 Experimental Results 35 2.5 Discussion 41 2.6 Conclusion 45 Chapter 3 Semi-automatic decomposition method of pulmonary artery and vein using two level minimum spanning tree constructions for non-enhanced volumetric CT 46 3.1 Introduction 47 3.2 Related Studies 51 3.3 Artery and Vein Decomposition 55 3.4 An Efficient Decomposition Method 70 3.5 Evaluation 75 3.6 Discussion and Conclusion 85 References 88 Abstract in Korean 95Docto

Brain Neoplasm Classification & Detection of Accuracy on MRI Images

The abnormal, uncontrolled cell growth in the brain, commonly known n as a brain tumor, can lead to immense pressure on the various nerves and blood vessels, causing irreversible harm to the body. Early detection of brain tumors is the key to avoiding such compilations. Tumour detection can be done through various advanced Machine Learning and Image Processing algorithms. Mind Brain tumors have demonstrated testing to treat, to a great extent inferable from the organic qualities of these diseases, which frequently plan to restrict progress. To begin with, by invading one of the body's most significant organs, these growths are much of the time situated past the compass of even the most gifted neurosurgeon. These cancers are likewise situated behind the blood-cerebrum boundary (BBB), a tight intersection and transport proteins that shield fragile brain tissues from openness to factors in the overall flow, subsequently obstructing openness to foundational chemotherapy [6,7]. Besides, the interesting formative, hereditary, epigenetic and micro environmental elements of the cerebrum much of the time render these tumors impervious to ordinary and novel medicines. These difficulties are accumulated by the uncommonness of cerebrum growths comparative with numerous different types of disease, restricting the degree of subsidizing and interest from the drug business and drawing in a moderately little and divided research local area

Automatic Construction of Immobilisation Masks for use in Radiotherapy Treatment of Head-and-Neck Cancer

Current clinical practice for immobilisation for patients undergoing brain or head and neck radiotherapy is normally achieved using Perspex or thermoplastic shells that are moulded to patient anatomy during a visit to the mould room. The shells are “made to measure” and the methods currently employed to make them require patients to visit the mould room. The mould room visit can be depressing and some patients find this process particularly unpleasant. In some cases, as treatment progresses, the tumour may shrink and therefore there may be a need for a further mould room visits. With modern manufacturing and rapid prototyping comes the possibility of determining the shape of the shells from the CT-scan of the patient directly, alleviating the need for making physical moulds from the patients’ head. However, extracting such a surface model remains a challenge and is the focus of this thesis. The aim of the work in this thesis is to develop an automatic pipeline capable of creating physical models of immobilisation shells directly from CT scans. The work includes an investigation of a number of image segmentation techniques to segment the skin/air interface from CT images. To enable the developed pipeline to be quantitatively evaluated we compared the 3D model generated from the CT data to ground truth obtained by 3D laser scans of masks produced by the mould room in the frame of a clinical trial. This involved automatically removing image artefacts due to fixations from CT imagery, automatic alignment (registration) between two meshes, measuring the degree of similarity between two 3D volumes, and automatic approach to evaluate the accuracy of segmentation. This thesis has raised and addressed many challenges within this pipeline. We have examined and evaluated each stage of the pipeline separately. The outcomes of the pipeline as a whole are currently being evaluated by a clinical trial (IRAS ID:209119, REC Ref.:16/YH/0485). Early results from the trial indicate that the approach is viable

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

A review of image processing methods for fetal head and brain analysis in ultrasound images

Background and objective: Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. Methods: In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. Results: For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. Conclusions: A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection. (c) 2022 Elsevier B.V. All rights reserved.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work was funded by projects “NORTE-01–0145-FEDER- 0 0 0 059 , NORTE-01-0145-FEDER-024300 and “NORTE-01–0145- FEDER-0 0 0 045 , supported by Northern Portugal Regional Opera- tional Programme (Norte2020), under the Portugal 2020 Partner- ship Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020 and UIDP/05549/2020 . The authors also acknowledge support from FCT and the Euro- pean Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018 and SFRH/BD/136721/2018