One major hallmark of the Alzheimer's disease (AD) is the loss of neurons in the brain. In many cases, medical experts
use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the
structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the
high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular
structure will directly lead to ventricle enlargement. For quantitative analysis, volume is the common choice. However,
volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape
descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features
are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer's disease has
been proposed. Training process is carried out to generate two probability maps for two training classes: healthy controls
(HC) and AD patients. By subtracting the HC probability map from the AD probability map, we get a 3D ventricle
discriminant map. Then a matching coefficient has been calculated between each training subject and the discriminant
map. An adjustable cut-off point of the matching coefficients has been drawn for the two classes. Generally, the higher
the cut-off point that has been drawn, the higher specificity can be achieved. However, it will result in relatively lower
sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC
curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method.
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our
method is motivated by the observation that neighboring or coupling objects in images generate configurations
and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs
coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate
kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape
distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on
such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm
based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted
objects in a number of applications. In particular for medical image analysis, we use our method to
extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging
segmentation problem. We also apply our technique to the problem of handwritten character segmentation.
Finally, we use our method to segment cars in urban scenes.
Diagnosis accuracy in the medical field, is mainly affected by either lack of sufficient understanding of some diseases or the inter/intra-observer variability of the diagnoses. We believe that mining of large medical databases can help improve the current
status of disease understanding and decision making. In a previous study based on binary description of hypointensity in the brain, it was shown that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in clinics. This paper
proposes a novel, nonbinary description of hypointensity in the brain based on principal component analysis. We compare the complementary and redundant information provided by the two
descriptions using Kendall's rank correlation coefficient in order to better understand the individual descriptions of iron accumulation in the brain and obtain a more robust and accurate search and retrieval system.
In the medical world, the accuracy of diagnosis is mainly affected by either lack of sufficient understanding of
some diseases or the inter-, and/or intra-observer variability of the diagnoses. The former requires understanding
the progress of diseases at much earlier stages, extraction of important information from ever growing amounts
of data, and finally finding correlations with certain features and complications that will illuminate the disease
progression. The latter (inter-, and intra- observer variability) is caused by the differences in the experience
levels of different medical experts (inter-observer variability) or by mental and physical tiredness of one expert
(intra-observer variability). We believe that the use of large databases can help improve the current status
of disease understanding and decision making. By comparing large number of patients, some of the otherwise
hidden relations can be revealed that results in better understanding, patients with similar complications can
be found, the diagnosis and treatment can be compared so that the medical expert can make a better diagnosis.
To this effect, this paper introduces a search and retrieval system for brain MR databases and shows that brain
iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain
iron load, that are commonly used in the clinics. We propose to use Kendall's correlation value to automatically
compare various returns to a query. We also describe a fully automated and fast brain MR image analysis system
to detect degenerative iron accumulation in brain, as it is the case in Alzheimer's and Parkinson's. The system
is composed of several novel image processing algorithms and has been extensively tested in Leiden University
Medical Center over so far more than 600 patients.
The performance of computer aided lung nodule detection (CAD) and
computer aided nodule volumetry is compared between standard-dose
(70-100 mAs) and ultra-low-dose CT images (5-10 mAs). A direct
quantitative performance comparison was possible, since for each
patient both an ultra-low-dose and a standard-dose CT scan were
acquired within the same examination session. The data sets were
recorded with a multi-slice CT scanner at the Charite university
hospital Berlin with 1 mm slice thickness. Our computer aided
nodule detection and segmentation algorithms were deployed on both
ultra-low-dose and standard-dose CT data without any dose-specific
fine-tuning or preprocessing. As a reference standard 292 nodules
from 20 patients were visually identified, each nodule both in
ultra-low-dose and standard-dose data sets. The CAD performance was
analyzed by virtue of multiple FROC curves for different lower
thresholds of the nodule diameter. For nodules with a
volume-equivalent diameter equal or larger than 4 mm (149 nodules
pairs), we observed a detection rate of 88% at a median false
positive rate of 2 per patient in standard-dose images, and 86%
detection rate in ultra-low-dose images, also at 2 FPs per patient.
Including even smaller nodules equal or larger than 2 mm (272
nodules pairs), we observed a detection rate of 86% in
standard-dose images, and 84% detection rate in ultra-low-dose
images, both at a rate of 5 FPs per patient.
Moreover, we observed a
correlation of 94% between the volume-equivalent nodule diameter as
automatically measured on ultra-low-dose versus on standard-dose
images, indicating that ultra-low-dose CT is also feasible for
growth-rate assessment in follow-up examinations. The comparable
performance of lung nodule CAD in ultra-low-dose and standard-dose
images is of particular interest with respect to lung cancer
screening of asymptomatic patients.
This paper introduces image processing methods to automatically detect the 3D volume-of-interest (VOI) and 2D region-of-interest (ROI) for deep gray matter organs (thalamus, globus pallidus, putamen, and caudate nucleus) of patients with suspected iron deposition from MR dual echo images. Prior to the VOI and ROI detection, cerebrospinal fluid (CSF) region is segmented by a clustering algorithm. For the segmentation, we automatically determine the cluster centers with the mean shift algorithm that can quickly identify the modes of a distribution. After the identification of the modes, we employ the K-Harmonic means clustering algorithm to segment the volumetric MR data into CSF and non-CSF. Having the CSF mask and observing that the frontal lobe of the lateral ventricle has more consistent shape accross age and pathological abnormalities, we propose a shape-directed landmark detection algorithm to detect the VOI in a speedy manner. The proposed landmark detection algorithm utilizes a novel shape model of the front lobe of the lateral ventricle for the slices where thalamus, globus pallidus, putamen, and caudate nucleus are expected to appear. After this step, for each slice in the VOI, we use horizontal and vertical projections of the CSF map to detect the approximate locations of the relevant organs to define the ROI. We demonstrate the robustness of the proposed VOI and ROI localization algorithms to pathologies, including severe amounts of iron accumulation as well as white matter lesions, and anatomical variations. The proposed algorithms achieved very high detection accuracy, 100% in the VOI detection , over a large set of a challenging MR dataset.
Automatic extraction of the tracheobronchial tree from high resolution CT data serves visual inspection by virtual endoscopy as well as computer aided measurement of clinical parameters along the airways. The purpose of this study is to show the feasibility of automatic extraction (segmentation) of the airway tree even in ultra-low-dose CT data (5-10 mAs), and to compare the performance of the airway extraction between ultra-low-dose and standard-dose (70-100 mAs) CT data. A direct performance comparison (instead of a mere simulation) was possible since for each patient both an ultra-low-dose and a standard-dose CT scan were acquired within the same examination session. The data sets were recorded with a multi-slice CT scanner at the Charite university hospital Berlin with 1 mm slice thickness.
An automated tree extraction algorithm was applied to both the ultra-low-dose and the standard-dose CT data. No dose-specific parameter-tuning or image pre-processing was used. For performance comparison, the total length of all visually verified centerlines of each tree was accumulated for all airways beyond the tracheal carina. Correlation of the extracted total airway length for ultra-low-dose versus standard-dose for each patient showed that on average in the ultra-low-dose images 84% of the length of the standard-dose images was retrieved.
This paper proposes a purely image-based TV channel logo detection algorithm that can detect logos independently from their motion and transparency features. The proposed algorithm can robustly detect any type of logos, such as transparent and animated, without requiring any temporal constraints whereas known methods have to wait for the
occurrence of large motion in the scene and assume stationary logos. The algorithm models logo pixels as outliers from the actual scene content that is represented by multiple 3-D histograms in the YCBCR space. We use four scene histograms corresponding to each of the four corners because the content characteristics change from one image corner to another. A further novelty of the proposed algorithm is that we define image corners and the areas where we compute the scene histograms by a cinematic technique called Golden Section Rule that is used by professionals. The robustness of the proposed algorithm is demonstrated over a dataset of representative TV content.
We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level soccer video processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game, ii) all goals in a game, and iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust for soccer video processing. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g. goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and the robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured at different countries and conditions.
In this paper, we present a complete framework for automatic analysis of soccer video by using domain specific information. In the proposed framework, following shot boundary detection, soccer shots are classified into 3 classes using the ratio of grass-colored pixels in a frame, and the size and number of soccer objects detected in a shot. These classes are long shots, in-field medium shots, and others, such as out-of-field of close-up shots. The long shots and in-field medium shots are further processed to analyze their semantic content. We observe that different low-level processing algorithms may be required to process different shot classes. For example, we introduce different tracking algorithms for the long shots and in- field medium shots. Furthermore, frame registration onto a reference field model is not usually applicable to in-field medium shots, because the field lines may not be visible. The proposed framework enables development of more effective low-level processing algorithms for high-level scene understanding, which perform nearly in real time. The results show the increased accuracy and efficiency of the proposed methods.
We present a generic model to describe image and video content by a combination of semantic entities and low level features for semantically meaningful and fast retrieval. The proposed model includes semantic entities such as Object, Event and Actors to express relations between the first two. The use of Actors entity increases the efficiency of certain types of search, while the use of semantic and linguistic roles increases the expression capability of the model. The model also contains links to high-level media segments such as actions and interactions, and low level media segments such as elementary motion and reaction units, as well as low-level features such as motion parameters and trajectories. Based on this model, we propose image and video retrieval combining semantic and low-level information. The retrieval performance of our system is tested by using query-by-annotation, query-by-example, query-by-sketch, and a combination of them.
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