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Providing accurate image-guidance for soft-tissue interventions remains a complex task. Most of the time, preoperative models and planning data are no more valid during the surgical process due to motions and deformations of the organ of interest. In this paper, two core components of a computer-assisted system for liver surgery are presented. One is an ultrasound segmentation techniques that allows...
In vivo observation of cells in the Arabidopsis thaliana root, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of individual cells. Automating the process of cell tracking is an important step to create tools which will facilitate the analysis of cellspsila...
With the huge amount of cell images produced in bio-imaging, automatic methods for segmentation are needed in order to evaluate the content of the images with respect to types of cells and their sizes. Traditional PDE-based methods using level-sets can perform automatic segmentation, but do not perform well on images with clustered cells containing sub-structures. Furthermore, DIC images contain a...
This paper introduces a new method for automatic quantification of subcutaneous, visceral and non-visceral internal fat from MR-images acquired using the two point Dixon technique in the abdominal region. The method includes (1) a three dimensional phase unwrapping to provide water and fat images, (2) an image intensity inhomogeneity correction, and (3) a morphon based registration and segmentation...
This paper presents an improved version of our specific methodology to detect the vessel tree in retinal angiographies. The automatic analysis of retinal vessel tree facilitates the computation of the arteriovenous index, which is essential for the diagnosis several eye diseases. The developed system is inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels...
In studying the relationship between risk factors and breast cancer, the growth patterns of fat pads and glandular tissues are considered as important biomarkers. The aim of this study is to measure the growth pattern statistics of rat mammary pads and glandular tissues with magnetic resonance (MR) time sequence images. In this paper, we proposed methods containing sequential steps to extract and...
Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global...
Acute renal rejection is the most common reason for graft (transplanted kidney) failure after kidney transplantation, and early detection is crucial to survival of function in the transplanted kidney. The current techniques for early detection of acute renal rejection are not accurate. For example, clearances of inulin and DTPA require multiple blood and urine tests, and they provide information on...
Computer assisted or automated histological grading of tissue biopsies for clinical cancer care is a long-studied but challenging problem. It requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for...
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality,...
Monitoring of bacterial populations requires automated analysis tools that provide accurate cell type quantification results. Here, methods for automated image analysis and bacteria type classification are presented. The classification method employs several discriminative features, calculated from automatically segmented images, for class determination. The performance of the algorithm is evaluated...
Segmentation of pulmonary nodules in chest radiographs is a difficult task due to heavy noise and superposition of ribs, vessels, and other anatomical structures in lung field. In this paper, a polynomial fitting based ray-casting algorithm is proposed for pulmonary nodule segmentation in chest radiographs. Instead of directly detecting nodule edge points, the nodule intensity profiles are first fitted...
In this paper we present the coupled active contours (CAC) model, which is applied to segmentation of the endocardium in ultrasonic images assuming Rayleigh distributed intensities. Comparative experiments, both real and synthetic, with a standard prior model are presented. In the CAC model the prior acts, by affine transformation, on the same image information as the active contour, in addition to...
Region growing is a frequently used segmentation method for medical ultrasound images processing. The first step of region growing is selecting the seed point which is inside the breast lesion. Most of the region growing methods require manually selecting the seed point which needs human interaction. To make the segmentation completely automatic, we propose a new automatic seed point selecting method...
We have developed a method to render brain tumors from endoneurosonography. We propose to track an ultrasound probe in successive endoscopic images without relying on an external optic or magnetic tracking system. The probe is tracked using two different methods, one of them based on generalized Hough transform and the other one on particle filters. With the estimation of the pose of the ultrasound...
The importance of accurate early diagnostics of dyslexia that severely affects the learning abilities of children cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D Magnetic...
We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including...
The segmentation and analysis of blood vessels has received much attention in the research community. The results aid numerous applications for diagnosis and treatment of vascular diseases. Here we use level set propagation with local phase information to capture the boundaries of vessels. The basic notion is that local phase, extracted using quadrature filters, allows us to distinguish between lines...
Cellular endocytosis is a mechanism of great interest in biology, for it regulates the communication between the cell and the external medium. With recent advances in fluorescence microscopy, endocytosis has become a popular candidate for image-based high content screening campains. In this context, we have developed an automated framework comprising robust cell segmentation using coupled shape-constrained...
We present a multiscale unsupervised segmenter for automatic detection of potentially cancerous regions of interest containing fibroglandular tissue in digital screening mammography. The mammogram tissue textures are locally represented by four causal multispectral random field models recursively evaluated for each pixel and several scales. The segmentation part of the algorithm is based on the underlying...
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