Presentation of detailed anatomical structures via 3-D models helps navigation and deployment of the prosthetic valve in
TAVI procedures. Fast and automatic contrast detection in the aortic root on X-ray images facilitates a seamless
workflow to utilize the 3-D models by triggering 2-D/3-D registration automatically when motion compensation is
needed. In this paper, we propose a novel method for automatic detection of contrast injection in the aortic root on
fluoroscopic and angiographic sequences. The proposed method is based on histogram analysis and likelihood ratio test,
and is robust to variations in the background, the density and volume of the injected contrast, and the size of the aorta.
The performance of the proposed algorithm was evaluated on 26 sequences from 5 patients and 3 clinical sites, with 16
out of 17 contrast injections correctly detected and zero false detections. The proposed method is of general form and
can be extended for detection of contrast injection in other organs and/or applications.
A major issue in telepathology is the extremely large and growing size of digitized "virtual" slides, which can require
several gigabytes of storage and cause significant delays in data transmission for remote image interpretation and
interactive visualization by pathologists. Compression can reduce this massive amount of virtual slide data, but
reversible (lossless) methods limit data reduction to less than 50%, while lossy compression can degrade image quality
and diagnostic accuracy. "Visually lossless" compression offers the potential for using higher compression levels
without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. We
investigated the utility of a visual discrimination model (VDM) and other distortion metrics for predicting JPEG 2000 bit
rates corresponding to visually lossless compression of virtual slides for breast biopsy specimens. Threshold bit rates
were determined experimentally with human observers for a variety of tissue regions cropped from virtual slides. For test
images compressed to their visually lossless thresholds, just-noticeable difference (JND) metrics computed by the VDM
were nearly constant at the 95th percentile level or higher, and were significantly less variable than peak signal-to-noise
ratio (PSNR) and Structural Similarity (SSIM) metrics. Our results suggest that VDM metrics could be used to guide the
compression of virtual slides to achieve visually lossless compression while providing 5 to 12 times the data reduction of
A major issue in telepathology is the extreme size of digitized slides, which require several gigabytes of storage and
cause significant delays in image delivery to pathologists. We investigated the utility of a visual discrimination model
(VDM) to predict bit rates for visually lossless JPEG2000 compression of breast biopsy virtual slides. Visually lossless
bit rates were determined experimentally with human observers. VDM metrics computed for those bit rates were nearly
constant, suggesting that VDMs could be used to achieve visually lossless image quality while providing about four
times the data reduction of reversible compression.
A system for automatic detection of pelvic lymph nodes is developed by incorporating complementary information
extracted from multiple MR sequences. A single MR sequence lacks sufficient diagnostic information for lymph
node localization and staging. Correct diagnosis often requires input from multiple complementary sequences
which makes manual detection of lymph nodes very labor intensive. Small lymph nodes are often missed even by
highly-trained radiologists. The proposed system is aimed at assisting radiologists in finding lymph nodes faster
and more accurately. To the best of our knowledge, this is the first such system reported in the literature. A
3-dimensional (3D) MR angiography (MRA) image is employed for extracting blood vessels that serve as a guide
in searching for pelvic lymph nodes. Segmentation, shape and location analysis of potential lymph nodes are then
performed using a high resolution 3D T1-weighted VIBE (T1-vibe) MR sequence acquired by Siemens 3T scanner.
An optional contrast-agent enhanced MR image, such as post ferumoxtran-10 T2*-weighted MEDIC sequence, can also be incorporated to further improve detection accuracy of malignant nodes. The system outputs a list of potential lymph node locations that are overlaid onto the corresponding MR sequences and presents them
to users with associated confidence levels as well as their sizes and lengths in each axis. Preliminary studies
demonstrates the feasibility of automatic lymph node detection and scenarios in which this system may be used
to assist radiologists in diagnosis and reporting.
Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest
visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection
in mass screening. Different methods have been proposed for MAs detection. The core technology for most of
existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that
requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often
cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper,
a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA
regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and
contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic
analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat"
based algorithm in both detection accuracy and computational speed.
The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for
a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular
complication very often seen in diabetes patients, is the most common cause of visual loss in working age population
of developed countries today. Since the possibility of slowing or even stopping the progress of this disease
depends on the early detection of DR, an automatic analysis of fundus images would be of great help to the
ophthalmologist due to the small size of the symptoms and the large number of patients. An important symptom
for DR are abnormally wide veins leading to an unusually low ratio of the average diameter of arteries to veins
(AVR). There are also other diseases like high blood pressure or diseases of the pancreas with one symptom being
an abnormal AVR value. To determine it, a classification of vessels as arteries or veins is indispensable. As to our
knowledge despite the importance there have only been two approaches to vessel classification yet. Therefore we
propose an improved method. We compare two feature extraction methods and two classification methods based
on support vector machines and neural networks. Given a hand-segmentation of vessels our approach achieves
95.32% correctly classified vessel pixels. This value decreases by 10% on average, if the result of a segmentation
algorithm is used as basis for the classification.
A robust and computationally efficient algorithm is proposed for
optic disk detection in retinal fundus images. The algorithm
includes two steps: optic disk localization and boundary detection.
In the localization step, vessels are modeled as a tree structure
and the root of the vessel tree is detected automatically and served
as the location of an optic disk. The implementation is based on an
efficient multi-level binarization and A* search algorithm. In the
boundary detection step, a circle is used to model the shape of an
optic disk, and Radon transform is applied to estimate the center
and radius of the circle. Experimental results of 48 retinal
images with varying image qualities show 100% accuracy in
localization and an accuracy of 92.36% in boundary detection. The
success of the proposed algorithm is attributed to the robust
features extracted from retinal images.