Statistical Region Based Segmentation of Ultrasound Images

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio, and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correla- tion. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropri- ate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental results demon- strating the effectiveness of our method, and compare the results to other parametric and non-parametric active contours

Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Significant edges in the case of a non-stationary Gaussian noise

In this paper, we propose an edge detection technique based on some local smoothing of the image followed by a statistical hypothesis testing on the gradient. An edge point being defined as a zero-crossing of the Laplacian, it is said to be a significant edge point if the gradient at this point is larger than a threshold s(\eps) defined by: if the image $I$ is pure noise, then \P(\norm{\nabla I}\geq s(\eps) \bigm| \Delta I = 0) \leq\eps. In other words, a significant edge is an edge which has a very low probability to be there because of noise. We will show that the threshold s(\eps) can be explicitly computed in the case of a stationary Gaussian noise. In images we are interested in, which are obtained by tomographic reconstruction from a radiograph, this method fails since the Gaussian noise is not stationary anymore. But in this case again, we will be able to give the law of the gradient conditionally on the zero-crossing of the Laplacian, and thus compute the threshold s(\eps). We will end this paper with some experiments and compare the results with the ones obtained with some other methods of edge detection

A total variation-undecimated wavelet approach to chest radiograph image enhancement

Most often medical images such as X-Rays have a low dynamic range and many of their targeted features are difficult to identify. Intensity transformations that improve image quality usually rely onwavelet denoising and enhancement typically use the technique of thresholding to obtain better quality medical images. A disadvantage of wavelet thresholding is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities. We utilize a total variation method and an undecimated wavelet image enhancing algorithm for improving the image quality of chest radiographs. Our approach achieves a high level chest radiograph image deniosing in lung nodules detection while preserving the important features. Moreover, our method results in a high image sensitivity that reduces the average number of false positives on a test set of medical data

PDE-based preprocessing of medical images

Medical imaging often requires a preprocessing step where filters are applied that remove noise while preserving semantically important structures such as edges. This may help to simplify subsequent tasks such as segmentation. One class of recent adaptive denoising methods consists of methods based on nonlinear partial differential equations (PDEs). In the present paper we survey our recent results on PDE-based preprocessing methods that may be applied to medical imaging problems. We focus on nonlinear diffusion filters and variational restoration methods. We explain the basic ideas, sketch some algorithmic aspects, illustrate the concepts by applying them to medical images such as mammograms, computerized tomography (CT), and magnetic resonance (MR) images. In particular we show the use of these filters as preprocessing steps for segmentation algorithms

A Comparison of Wavelet and Simplicity-Based Heart Sound and Murmur Segmentation Methods

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments. The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs