Unsupervised delineation of the vessel tree in retinal fundus images

Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding.We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.peer-reviewe

MEDICAL IMAGE PROCESSING USING MATLAB

MATLAB and the Image Processing Toolbox provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. The interactive tools allowed us to perform spatial image transformations, morphological operations such as edge detection and noise removal, region-of-interest processing, filtering, basic statistics, curve fitting, FFT, DCT and Radon Transform. Making graphics objects semitransparent is a useful technique in 3-D visualization which furnishes more information about spatial relationships of different structures. The toolbox functions implemented in the open MATLAB language has also been used to develop the customized algorithms.Histogram, 3-D Surface Plot, Round-off Noise Power Spectrum

Ehrenzweig and the Statute of Frauds: An Inquiry Into the Rule of Validation

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges

Research Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain

Medical Image analysis and processing has great significance in the field of medicine, especially in Non- invasive treatment and clinical study. Medical imaging techniques and analysis tools enable the Doctors and Radiologists to arrive at a specific diagnosis. Medical Image Processing has emerged as one of the most important tools to identify as well as diagnose various disorders. Imaging helps the Doctors to visualize and analyze the image for understanding of abnormalities in internal structures. The medical images data obtained from Bio-medical Devices which use imaging techniques like Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Mammogram, which indicates the presence or absence of the lesion along with the patient history, is an important factor in the diagnosis. The algorithm proposes the use of Digital Image processing tools for the identification of Hemorrhage and Infarct in the human brain, by using a semi-automatic seeded region growing algorithm for the processing of the clinical images. The algorithm has been extended to the Real-Time Data of CT brain images and uses an intensity-based growing technique to identify the infarct and hemorrhage affected area, of the brain. The objective of this paper is to propose a seeded region growing algorithm to assist the Radiologists in identifying the Hemorrhage and Infarct in the human brain and to arrive at a decision faster and accurate.¢Lp¤

Teaching Signal Processing to the Medical Profession

Knowledge of signal processing is very important for medical students. A medical signal may be used for monitoring, constructing an image, or for extracting the numerical quantity of a parameter. This information forms a basis for medical decisions. However, the processing of the signal may lead to distortion and an incorrect interpretation. The present article describes an educational practical for first year medical students. It uses the electrocardiogram, which can be obtained easily, as a convenient example of a medical signal. The practical was developed at the VU University Amsterdam and summarizes the elementary concepts of signal processing