Open Access. Powered by Scholars. Published by Universities.®
Electromagnetics and Photonics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Aliased images (1)
- Antialiasing (1)
- Computed Tomography (1)
- Computer Aided Detection System (1)
- Cyclic coordinate-descent optimization (1)
-
- Denoising (1)
- Detector array (1)
- Detectors (1)
- Error analysis (1)
- Experimental results (1)
- Fischer Linear Discriminant Classifier (1)
- Focal planes (1)
- Gradient descent optimization (1)
- High-resolution image estimation (1)
- High-resolution imaging (1)
- Image registration (1)
- Image registration parameters (1)
- Image resolution (1)
- Image sampling (1)
- Image sequences (1)
- Imaging systems (1)
- Infrared focal plane arrays (1)
- Infrared imaging (1)
- Iterative algorithms (1)
- Layout (1)
- Lung Cancer (1)
- MAP registration (1)
- Maximum a posteriori estimation (1)
- Maximum likelihood estimation (1)
- Neural Network. (1)
Articles 1 - 4 of 4
Full-Text Articles in Electromagnetics and Photonics
Analysis Of Various Classification Techniques For Computer Aided Detection System Of Pulmonary Nodules In Ct, Barath Narayanan Narayanan, Russell C. Hardie, Temesguen Messay
Analysis Of Various Classification Techniques For Computer Aided Detection System Of Pulmonary Nodules In Ct, Barath Narayanan Narayanan, Russell C. Hardie, Temesguen Messay
Russell C. Hardie
Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) …
Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie
Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie
Russell C. Hardie
In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video …
Segmentation Of Pulmonary Nodules In Computed Tomography Using A Regression Neural Network Approach And Its Application To The Lung Image Database Consortium And Image Database Resource Initiative Dataset, Temesguen Messay, Russell C. Hardie, Timothy R. Tuinstra
Segmentation Of Pulmonary Nodules In Computed Tomography Using A Regression Neural Network Approach And Its Application To The Lung Image Database Consortium And Image Database Resource Initiative Dataset, Temesguen Messay, Russell C. Hardie, Timothy R. Tuinstra
Russell C. Hardie
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system.
If improved segmentation results are needed, the SA …
Joint Map Registration And High Resolution Image Estimation Using A Sequence Of Undersampled Images, Russell C. Hardie, Kenneth J. Barnard, Ernest E. Armstrong
Joint Map Registration And High Resolution Image Estimation Using A Sequence Of Undersampled Images, Russell C. Hardie, Kenneth J. Barnard, Ernest E. Armstrong
Russell C. Hardie
n many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the high-resolution image is presented. Several previous approaches have relied on knowing the registration parameters …