AN EFFICIENT SEGMENTATION OF RETINAL BLOOD VESSEL USING QUANTUM EVOLUTIONARY ALGORITHM PDF Download
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Author: G.V. Shrichandran Publisher: Infinite Study ISBN: Category : Languages : en Pages : 15
Book Description
Retinal blood vessels segmentation is mandatory for retinal image analysis, diagnose and treatment of specific diseases. However, the manual analysis of the retinal image is time consuming, therefore, it is necessary for developing an automatic analysis of retinal fundus images.
Author: G.V. Shrichandran Publisher: Infinite Study ISBN: Category : Languages : en Pages : 15
Book Description
Retinal blood vessels segmentation is mandatory for retinal image analysis, diagnose and treatment of specific diseases. However, the manual analysis of the retinal image is time consuming, therefore, it is necessary for developing an automatic analysis of retinal fundus images.
Author: SINDHU SARANYA Publisher: Infinite Study ISBN: Category : Languages : en Pages : 10
Book Description
Segmenting blood vessel from the retinal image is important for detecting many retinal vascular disorders. Diseases which are all affecting the blood vessels of the eye are known as Retinal vascular disorders.
Author: Aqsa Sultana Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Vascular network of human eye plays an important diagnostic role in ophthalmology. Various size of the vessels, relatively low contrast and the presence of potential retinal diseases or pathologies complicate the segmentation process in fundus imaging. It is impossible to segment the vascular ends of thin retina vessels with the existing computational methods due to their high inefficiency and precision. Deep learning provides superior performance in semantic segmentation, especially for biomedical applications. One of the popular deep learning architectures for semantic segmentation is U-Net, which is specifically tailored for feature cascading to perform effective pixel classification. Advanced versions of U-Net such as Recurrent U-Net (RU-Net) and Recurrent Residual U-Net (R2U-Net) had been proposed for improved performance. The studies state that learning from a significant depth and extensive network with residual units is more accurate and can extract more discriminative feature representation for segmentation than learning from a shallow network without the residual units. In other words, residual learning reinforces the features in the previous layers to extract more versatile characteristics. It is observed that the reinforcement of features in successive layers would provide a relatively faster and efficient performance in image segmentation. In this thesis, we propose a modified U- Net architecture incorporating the residues from successive layers for the extraction of features in subsequent layers. The new model, named as Residues in Succession U-Net, is optimized for better overall performance exhibiting qualitative and quantitative results with the same number of parameters. 4 The Residues in Succession U-Net is evaluated for blood vessel segmentation in retinal images on a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE). The testing and evaluation results show that the new model provides improved performance when compared to U-Net and R2U-Net in the same experimentation setup. Additionally, the model is evaluated on preprocessed image data with a nonlinear image enhancement strategy, known as Integrated Neighborhood Dependent Approach for Nonlinear Enhancement of color images (AINDANE) to improve the fine details in the images. However, it is observed that the enhancement process boosted the noise components too which reduced the quality of learning performance of the network. Residues in succession U-Net evaluated on original data produced superior quantitative results when compared with other U-Net models. We are considering the application of a sophisticated enhancement strategy and use of a more effective loss function to improve the segmentation performance.
Author: Ishmeet Kaur Publisher: Infinite Study ISBN: Category : Languages : en Pages : 7
Book Description
Diabetic Retinopathy is a disease which causes a menace to the eyesight. The detection of this at an early stage can aid the person from vision loss. The examination of retinal blood vessel structure can help to detect the disease, so segmentation of retinal blood vessel vasculature is important and is appreciated by the ophthalmologists
Author: Yanhui Guo Publisher: Infinite Study ISBN: Category : Languages : en Pages : 10
Book Description
A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image’s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.â
Author: Ali Hatamizadeh Publisher: ISBN: Category : Languages : en Pages : 55
Book Description
The reliable segmentation and quantification of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. In this thesis, we address this problem in depth, leveraging the power of artificial intelligence to devise automated approaches for the segmentation and width estimation of vessels in two ophthalmological image modalities. First, we investigate the automated segmentation of retinal vessels in color fundus images. We propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder. We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions. The proposed methodology takes a whole-image approach and is tested on two publicly available datasets, DRIVE and CHASE-DB1. Second, we introduce the first deep-learning based method for the semantic segmentation of retinal arteries and veins in infrared imaging along with a novel dataset dubbed AVIR, and propose an innovative encoder-decoder that is regularized by variational autoencoders. Additionally, our method automatically quantifies the morphological changes of the segmented arteries and veins, which is important for establishing automated vessel tracking systems.
Author: Erik Cuevas Publisher: Springer Nature ISBN: 3030581004 Category : Technology & Engineering Languages : en Pages : 281
Book Description
This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective.
Author: Siddhartha Bhattacharyya Publisher: Academic Press ISBN: 0323851797 Category : Computers Languages : en Pages : 420
Book Description
The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. Provides insights into the theory, algorithms, implementation, and application of computational intelligence techniques Covers a wide range of applications of deep learning across various domains which are researching the applications of computational intelligence Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques
Author: Emanuele Trucco Publisher: Academic Press ISBN: 0081028164 Category : Computers Languages : en Pages : 504
Book Description
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies
Author: G.V. Shrichandran
Author: G.V. Shrichandran
Author: SINDHU SARANYA 
Author: Aqsa Sultana
Author: Ishmeet Kaur
Author: Yanhui Guo
Author: Henner Hanssen
Author: Ali Hatamizadeh
Author: Erik Cuevas
Author: Siddhartha Bhattacharyya
Author: Emanuele Trucco