Segmentation plays an important role in medical imaging, a precise segmentation can significantly improve the accuracy
of object detection and localization. Level set based model is robust in image segmentation, but the parameters
of level set function are usually decided by empirical method, which discourages its application in medical area, because
medical images are various and the users may not be familiar with parameters setting of level set method. In this paper,
we present an automatic segmentation method based on variational level set formulation. This method is formulated by
statistical measures and solved by using the Euler-Lagrange equation. The segmentation criteria of our method rely on
structural similarities of the image, which are luminance, contrast, and correlation coefficients. These criteria are formulated
into an energy function to maximize the structural difference between object and background in segmentation. The
energy function is solved and implemented by using variational level set method. Unlike prevalent level set methods, the
segmentation parameters of our approach are automatically decided by structural information of the image and updated
during iteration, so our model is nonparametric. Moreover, our approach does not necessitate any training, nor any a priori
assumption about probability density functions of statistical inference. Furthermore, our method is region-based without
using gradients, and the parameters in our method are updated according to image information, so our method can significantly
reduce computation costs in its numerical implementation. The segmentation results have shown that our method
adequately captures the structural differences between object and background during segmentation.
Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides
an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on
determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have
been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to
large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from
prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we
analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel
based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and
lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such
as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and
solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method
is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that
our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with
expert traced manual AIF.
Recently, there is an increasing need to share medical images for research purpose. In order to respect and preserve
patient privacy, most of the medical images are de-identified with protected health information (PHI) before
research sharing. Since manual de-identification is time-consuming and tedious, so an automatic de-identification
system is necessary and helpful for the doctors to remove text from medical images. A lot of papers have been
written about algorithms of text detection and extraction, however, little has been applied to de-identification of
medical images. Since the de-identification system is designed for end-users, it should be effective, accurate and
fast. This paper proposes an automatic system to detect and extract text from medical images for de-identification
purposes, while keeping the anatomic structures intact. First, considering the text have a remarkable contrast with
the background, a region variance based algorithm is used to detect the text regions. In post processing, geometric
constraints are applied to the detected text regions to eliminate over-segmentation, e.g., lines and anatomic
structures. After that, a region based level set method is used to extract text from the detected text regions. A GUI
for the prototype application of the text detection and extraction system is implemented, which shows that our method can detect most of the text in the images. Experimental results validate that our method can detect and extract text in medical images with a 99% recall rate. Future research of this system includes algorithm improvement, performance evaluation, and computation optimization.
In this study, the radial basis functions based SG algorithm (SGRBF) is applied for evolution of level sets in image
segmentation. The implementation of level set method in image processing often involves solving partial differential
equations (PDEs). Finite differences implicit scheme is a prevalent method to solve PDE for extending the evolution of
level sets. Instead of using finite differences method, SGRBF is used in our study for evolving level sets. The SGRBF is
a mathematical framework developed for function approximation using Gaussian RBFs. In SGRBF, the number and
centers of the basis functions are determined in a systematic and mathematically sound way using a purely algebraic
approach. The numerical results show that, except for a continuous representation of both the implicit function and its
level sets, the algorithm we introduce here can reduce the computation cost by selecting the most contributive centers for
radial basis functions.
Abnormalities of the number and location of cells are hallmarks of both developmental and degenerative neurological diseases. However, standard stereological methods are impractical for assigning each cell's nucleus position within a large volume of brain tissue. We propose an automated approach for segmentation and localization of the brain cell nuclei in laser scanning microscopy (LSM) embryonic mouse brain images. The nuclei in these images are first segmented by using the level set (LS) and watershed methods in each optical plane. The segmentation results are further refined by application of information from adjacent optical planes and prior knowledge of nuclear shape. Segmentation is then followed with an algorithm for 3D localization of the centroid of nucleus (CN). Each volume of tissue is thus represented by a collection of centroids leading to an approximate 10,000-fold reduction in the data set size, as compared to the original image series. Our method has been tested on LSM images obtained from an embryonic mouse brain, and compared to the segmentation and CN localization performed by an expert. The average Euclidian distance between locations of CNs obtained using our method and those obtained by an expert is 1.58±1.24 µm, a value well within the ~5 µm average radius of each nucleus. We conclude that our approach accurately segments and localizes CNs within cell dense embryonic tissue.
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