10,407 research outputs found
Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system
Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient
Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods
Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case
Discrete Wavelet Transforms
The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications
A Review and Performance Analysis of Image Edge Detection Algorithms
Edge detection is the fundamental operation of digital image processing and applied in many fields like industrial, medical, satellite, agriculture etc. According to this growth of edge detection applications, many researchers and scholars are interested to develop the edge detection algorithm by using various techniques. This paper illustrates the review for what are the novel techniques are used for the edge detection, which operators are mostly used by them and how they get the accurate results to compare with existing methods. It also discussing the performance analysis of most commonly used edge detection operators such as Canny, Laplacian Gaussian (LoG), Sobel, Prewitt and Roberts,. Finally the accuracy, PSNR (Peak Signal to Noise Ratio) and execution time are tabulated and realize the most precious and fast computed edge detection method is uncovered
Performance Analysis of Cone Detection Algorithms
Many algorithms have been proposed to help clinicians evaluate cone density
and spacing, as these may be related to the onset of retinal diseases. However,
there has been no rigorous comparison of the performance of these algorithms.
In addition, the performance of such algorithms is typically determined by
comparison with human observers. Here we propose a technique to simulate
realistic images of the cone mosaic. We use the simulated images to test the
performance of two popular cone detection algorithms and we introduce an
algorithm which is used by astronomers to detect stars in astronomical images.
We use Free Response Operating Characteristic (FROC) curves to evaluate and
compare the performance of the three algorithms. This allows us to optimize the
performance of each algorithm. We observe that performance is significantly
enhanced by up-sampling the images. We investigate the effect of noise and
image quality on cone mosaic parameters estimated using the different
algorithms, finding that the estimated regularity is the most sensitive
parameter.
This paper was published in JOSA A and is made available as an electronic
reprint with the permission of OSA. The paper can be found at the following URL
on the OSA website: http://www.opticsinfobase.org/abstract.cfm?msid=224577.
Systematic or multiple reproduction or distribution to multiple locations via
electronic or other means is prohibited and is subject to penalties under law.Comment: 13 pages, 7 figures, 2 table
WARP: Wavelets with adaptive recursive partitioning for multi-dimensional data
Effective identification of asymmetric and local features in images and other
data observed on multi-dimensional grids plays a critical role in a wide range
of applications including biomedical and natural image processing. Moreover,
the ever increasing amount of image data, in terms of both the resolution per
image and the number of images processed per application, requires algorithms
and methods for such applications to be computationally efficient. We develop a
new probabilistic framework for multi-dimensional data to overcome these
challenges through incorporating data adaptivity into discrete wavelet
transforms, thereby allowing them to adapt to the geometric structure of the
data while maintaining the linear computational scalability. By exploiting a
connection between the local directionality of wavelet transforms and recursive
dyadic partitioning on the grid points of the observation, we obtain the
desired adaptivity through adding to the traditional Bayesian wavelet
regression framework an additional layer of Bayesian modeling on the space of
recursive partitions over the grid points. We derive the corresponding
inference recipe in the form of a recursive representation of the exact
posterior, and develop a class of efficient recursive message passing
algorithms for achieving exact Bayesian inference with a computational
complexity linear in the resolution and sample size of the images. While our
framework is applicable to a range of problems including multi-dimensional
signal processing, compression, and structural learning, we illustrate its work
and evaluate its performance in the context of 2D and 3D image reconstruction
using real images from the ImageNet database. We also apply the framework to
analyze a data set from retinal optical coherence tomography
Deteksi Tepi pada Citra Rontgen Penyakit COVID-19 Menggunakan Metode Sobel
Background: Coronavirus Disease 2019 (COVID-19) discovered at the end of 2019 occasioned by coronavirus 2 (SARS-CoV-2) causing severe acute respiratory syndrome and expanded globally so that World Health Organization (WHO) declare a global pandemic. There was a delay of socialization and delivering information to society about this disease. The doctors did a method to detect COVID-19 by reading the correct X-ray images of patients who affected by a coronavirus.Methods: With advances in the field of computers in the application of image processing techniques method of this research use application to get better digital image results for COVID-19 X-ray images, so make it easier to analyze the X-ray images. There are 13 samples of X-ray images that are processed through the clean the stage with high-pass filtering, then segmented with thresholding technique in the lung area, then the edge detection method is used to mark the area that makes the image detail.Results: The result of this detection form the pattern of objects and regions of the spread of coronavirus, then there is a limit on the image looks clear enough, with the Sobel method producing white pixels that are so visible as well.Conclusions: This study to make a simulation of x-ray thorax COVID-19 and know the region of virus infection using Sobel method with thresholding technique that can see the spread of coronavirus and shown that edge detection use Sobel method as one of diagnosing for COVID-19 disease
AM-FM Analysis of Structural and Functional Magnetic Resonance Images
This thesis proposes the application of multi-dimensional Amplitude-Modulation Frequency-Modulation (AM-FM) methods to magnetic resonance images (MRI). The basic goal is to provide a framework for exploring non-stationary characteristics of structural and functional MRI (sMRI and fMRI). First, we provide a comparison framework for the most popular AM-FM methods using different filterbank configurations that includes Gabor, Equirriple and multi-scale directional designs. We compare the performance and robustness to Gaussian noise using synthetic FM image examples. We show that the multi-dimensional quasi-local method (QLM) with an equiripple filterbank gave the best results in terms of instantaneous frequency (IF) estimation. We then apply the best performing AM-FM method to sMRI to compute the 3D IF features. We use a t-test on the IF magnitude for each voxel to find evidence of significant differences between healthy controls and patients diagnosed with schizophrenia (n=353) can be found in the IF. We also propose the use of the instantaneous phase (IP) as a new feature for analyzing fMRI images. Using principal component analysis and independent component analysis on the instantaneous phase from fMRI, we built spatial maps and identified brain regions that are biologically coherent with the task performed by the subject. This thesis provides the first application of AM-FM models to fMRI and sMRI
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