The number of people being affected by Diabetes mellitus worldwide is increasing at an alarming rate. Monitoring
of the diabetic condition and its effects on the human body are therefore of great importance. Of particular
interest is diabetic retinopathy (DR) which is a result of prolonged, unchecked diabetes and affects the visual
system. DR is a leading cause of blindness throughout the world. At any point of time 25 - 44% of people with
diabetes are afflicted by DR. Automation of the screening and monitoring process for DR is therefore essential for
efficient utilization of healthcare resources and optimizing treatment of the affected individuals. Such automation
would use retinal images and detect the presence of specific artifacts such as hard exudates, hemorrhages and soft
exudates (that may appear in the image) to gauge the severity of DR. In this paper, we focus on the detection
of hard exudates. We propose a two step system that consists of a screening step that classifies retinal images as
normal or abnormal based on the presence of hard exudates and a detection stage that localizes these artifacts
in an abnormal retinal image. The proposed screening step automatically detects the presence of hard exudates
with a high sensitivity and positive predictive value (PPV ). The detection/localization step uses a k-means
based clustering approach to localize hard exudates in the retinal image. Suitable feature vectors are chosen
based on their ability to isolate hard exudates while minimizing false detections. The algorithm was tested
on a benchmark dataset (DIARETDB1) and was seen to provide a superior performance compared to existing
methods. The two-step process described in this paper can be embedded in a tele-ophthalmology system to aid
with speedy detection and diagnosis of the severity of DR.
This paper addresses a solution to the problem of designing the low pass filter of a two-channel IIR orthogonal perfect reconstruction filter bank to match a specified frequency response. The derived solution treats independently the numerator and denominator in the rational transfer function. Based on the proposed solutions, a low pass filter is matched to a desired frequency response and the frequency response constrained to be have multiple zeroes at (pi) . The end result is a low pass filter with a smoother response function than FIR based solutions for a given number of filter coefficients while optimal in the sense of being close to desired functions. The paper concludes by demonstrating the solution by matching the low pass filter of a Meyer wavelet.
Finite Impulse Response (FIR) filters have been the major players in the Wavelets and Multiresoltion Analysis field; mainly due to their ease of design and understandable nature, as well as their well behaved characteristics such as stability and linear phase response. However, it has been demonstrated that in a number of cases IIR filters are more appropriate. This paper describes a new solution for the wavelet conditions to derive stable IIR filters that are matched in a least squares sense to specified frequency responses. The derived solution treats independently the numerator and denominator in the rational transfer function. This solution can be applied to any IIR wavelet filter bank satisfying the orthogonality and perfect reconstruction conditions. Based on the proposed solutions, a new IIR wavelet filter bank with the low pass filter matched to a desired frequency response is developed.
In this paper, we present closed form expressions for filters in multidimensional interpolation and approximation sampling systems matched to the input random field or image class in the mean squared sense. We then present expression for the mean squared error between the reconstructed and the input field. For the approximation sampling system we use this expression to show that the optimal antialiasing and reconstruction filters are spectral factors or an ideal brickwall-type of a filter. Finally, we give examples of filters matched to an image class generated using a spearable AR model and a quincunx sampling lattice and compare their performance with that of some standard interpolators.
This paper introduces a novel filter bank structure called the perfect reconstruction circular convolution (PRCC) filter bank. These filters satisfy the perfect reconstruction properties, namely, the paraunitary conditions, in the discrete frequency domain. The development of the PRCC framework has been motivated by the need for an efficient, invertible algorithm for the implementation of the discrete wavelet transform (DWT) based on bandlimited scaling functions and wavelets. As a motivation, we show how bandlimited scaling functions arise naturally in the context of interpolation and approximation sampling systems with the filters matched to the input process. Next, we show how the PRCC filter bank framework serves as a basis for a frequency sampled implementation of DWT based on bandlimited scaling functions and wavelets, and in general, of matched filters in the above sampling systems. Finally, we present simulation results sing the PRCC framework which verify that the matched interpolating function gives the smallest mean squared error between the input and the reconstructed signal, as compared to other interpolating functions.
Conference Committee Involvement (5)
Digital Photography and Mobile Imaging XI
9 February 2015 | San Francisco, California, United States
Digital Photography X
3 February 2014 | San Francisco, California, United States
Digital Photography IX
4 February 2013 | Burlingame, California, United States
Digital Photography VIII
23 January 2012 | Burlingame, California, United States
Digital Photography VII
24 January 2011 | San Francisco Airport, California, United States