Detection of tuberculosis (TB) on chest radiographs (CXRs) is a hard problem. Therefore, to help radiologists or even take their place when they are not available, computer-aided detection (CAD) systems are being developed. In order to reach a performance comparable to that of human experts, the pattern recognition algorithms of these systems are typically trained on large CXR databases that have been manually annotated to indicate the abnormal lung regions. However, manually outlining those regions constitutes a time-consuming process that, besides, is prone to inconsistencies and errors introduced by interobserver variability and the absence of an external reference standard. In this paper, we investigate an alternative pattern classi cation method, namely multiple-instance learning (MIL), that does not require such detailed information for a CAD system to be trained. We have applied this alternative approach to a CAD system aimed at detecting textural lesions associated with TB. Only the case (or image) condition (normal or abnormal) was provided in the training stage. We compared the resulting performance with those achieved by several variations of a conventional system trained with detailed annotations. A database of 917 CXRs was constructed for experimentation. It was divided into two roughly equal parts that were used as training and test sets. The area under the receiver operating characteristic curve was utilized as a performance measure. Our experiments show that, by applying the investigated MIL approach, comparable results as with the aforementioned conventional systems are obtained in most cases, without requiring condition information at the lesion level.
As the importance of Computer Aided Detection (CAD) systems application is rising in medical imaging field due to the advantages they generate; it is essential to know their weaknesses and try to find a proper solution for them. A common possible practical problem that affects CAD systems performance is: dissimilar training and testing datasets declines the efficiency of CAD systems. In this paper normalizing images is proposed, three different normalization methods are applied on chest radiographs namely (1) Simple normalization (2) Local Normalization (3) Multi Band Local Normalization. The supervised lung segmentation CAD system performance is evaluated on normalized chest radiographs with these three different normalization methods in terms of Jaccard index. As a conclusion the normalization enhances the performance of CAD system and among these three normalization methods Local Normalization and Multi band Local normalization improve performance of CAD system more significantly than the simple normalization.
Computer aided detection (CAD) of tuberculosis (TB) on chest radiographs (CXR) is difficult because the disease has varied manifestations, like opacification, hilar elevation, and pleural effusions. We have developed a CAD research prototype for TB (CAD4TB v1.08, Diagnostic Image Analysis Group, Nijmegen, The Netherlands) which is trained to detect textural abnormalities inside unobscured lung fields. If the only abnormality visible on a CXR would be a blunt costophrenic angle, caused by pleural fluid in the costophrenic recess, this is likely to be missed by texture analysis in the lung fields. The goal of this work is therefore to detect the presence of blunt costophrenic (CP) angles caused by pleural effusion on chest radiographs. The CP angle is the angle formed by the hemidiaphragm and the chest wall. We define the intersection point of both as the CP angle point. We first detect the CP angle point automatically from a lung field segmentation by finding the foreground pixel of each lung with maximum y location. Patches are extracted around the CP angle point and boundary tracing is performed to detect 10 consecutive pixels along the hemidiaphragm and the chest wall and derive the CP angle from these. We evaluate the method on a data set of 250 normal CXRs, 200 CXRs with only one or two blunt CP angles and 200 CXRs with one or two blunt CP angles but also other abnormalities. For these three groups, the CP angle location and angle measurements were accurate in 91%, 88%, and 92% of all the cases, respectively. The average CP angles for the three groups are indeed different with 71.6° ± 22.9, 87.5° ± 25.7, and 87.7° ± 25.3, respectively.
Computer aided detection (CAD) of tuberculosis (TB) on chest radiographs (CXR) is challenging due to over-lapping structures. Suppression of normal structures can reduce overprojection effects and can enhance the appearance of diffuse parenchymal abnormalities. In this work, we compare two CAD systems to detect textural abnormalities in chest radiographs of TB suspects. One CAD system was trained and tested on the original CXR and the other CAD system was trained and tested on bone suppression images (BSI). BSI were created using a commercially available software (ClearRead 2.4, Riverain Medical). The CAD system is trained with 431 normal and 434 abnormal images with manually outlined abnormal regions. Subtlety rating (1-3) is assigned to each abnormal region, where 3 refers to obvious and 1 refers to subtle abnormalities. Performance is evaluated on normal and abnormal regions from an independent dataset of 900 images. These contain in total 454 normal and 1127 abnormal regions, which are divided into 3 subtlety categories containing 280, 527 and 320 abnormal regions, respectively. For normal regions, original/BSI CAD has an average abnormality score of 0.094±0.027/0.085±0.032 (p − 5.6×10−19). For abnormal regions, subtlety 1, 2, 3 categories have average abnormality scores for original/BSI of 0.155±0.073/0.156±0.089 (p = 0.73), 0.194±0.086/0.207±0.101 (p = 5.7×10−7), 0.225±0.119/0.247±0.117 (p = 4.4×10−7), respectively. Thus for normal regions, CAD scores slightly decrease when using BSI instead of the original images, and for abnormal regions, the scores increase slightly. We therefore conclude that the use of bone suppression results in slightly but significantly improved automated detection of textural abnormalities in chest radiographs.
The clinical use of computer-aided diagnosis (CAD) systems is increasing. A possible limitation of CAD systems is that they are typically trained on data from a small number of sources and as a result, they may not perform optimally on data from different sources. In particular for chest radiographs, it is known that acquisition settings, detector technology, proprietary post-processing and, in the case of analog images, digitization, can all influence the appearance and statistical properties of the image. In this work we investigate if a simple energy normalization procedure is sufficient to increase the robustness of CAD in chest radiography. We evaluate the performance of a supervised lung segmentation algorithm, trained with data from one type of machine, on twenty images each from five different sources. The results, expressed in terms of Jaccard index, increase from 0.530 ± 0.290 to 0.914 ± 0.041 when energy normalization is omitted or applied, respectively. We conclude that energy normalization is an effective way to make the performance of lung segmentation satisfactory on data from different sources.
Computer-aided diagnosis (CAD) systems for detection of lung nodules have been an active topic of research for last few
years. It is desirable that a CAD system should generate very low false positives (FPs) while maintaining high
sensitivity. This work aims to reduce the number of false positives occurring at vessel bifurcation point. FPs occur quite
frequently on vessel branching point due to its shape which can appear locally spherical due to the intrinsic geometry of
intersecting tubular vessel structures combined with partial volume effects and soft tissue attenuation appearance
surrounded by parenchyma.
We propose a model-based technique for detection of vessel branching points using skeletonization, followed by branch-point
analysis. First we perform vessel structure enhancement using a multi-scale Hessian filter to accurately segment
tubular structures of various sizes followed by thresholding to get binary vessel structure segmentation [6]. A modified
Reebgraph [7] is applied next to extract the critical points of structure and these are joined by a nearest neighbor criterion
to obtain complete skeletal model of vessel structure. Finally, the skeletal model is traversed to identify branch points,
and extract metrics including individual branch length, number of branches and angle between various branches. Results
on 80 sub-volumes consisting of 60 actual vessel-branching and 20 solitary solid nodules show that the algorithm
identified correctly vessel branching points for 57 sub-volumes (95% sensitivity) and misclassified 2 nodules as vessel
branch. Thus, this technique has potential in explicit identification of vessel branching points for general vessel analysis, and could be useful in false positive reduction in a lung CAD system.
Common chest CT clinical workflows for detecting lung nodules use a large slice thickness protocol (typically 5 mm).
However, most existing CAD studies are performed on a thin slice data (0.3-2 mm) available on state-of-the art scanners.
A major challenge for the widespread clinical use of Lung CAD is the concurrent availability of both thick and thin
resolutions for use by radiologist and CAD respectively. Having both slice thickness reconstructions is not always
possible based on the availability of scanner technologies, acquisition parameters chosen at remote site, and transmission
and archiving constraints that may make transmission and storage of large data impracticable. However, applying current
thin-slice CAD algorithms on thick slice cases outside their designed acquisition parameters may result in degradation of
sensitivity and high false-positive rate making them clinically unacceptable. Therefore a CAD system that can handle
thicker slice acquisitions is desirable to address those situations.
In this paper, we propose a CAD system which works directly on thick slice scans. We first propose a multi-stage
classifier based CAD system for detecting lung nodules in such data. Furthermore, we propose different gating systems
adapted for thick slice scans. The proposed gating schemes are based on: 1. wall-attached and non wall-attached. 2.
central and non-central region. These gating schemes can be used independently or combined as well. Finally, we present
prototype1 results showing significant improvement of CAD sensitivity at much better false positive rate on thick-slice
CT images are presented.
In this paper we propose a technique for automatic detection of intracranial hemorrhage (ICH) and acute
intracranial hemorrhage (AIH) in brain Computed Tomography (CT) for trauma cases where no contrast can be
applied and the CT has large slice thickness. ICH or AIH comprise of internal bleeding (intra-axial) or external
(extra-axial) to the brain substance. Large bleeds like in intra-axial region are easy to diagnose whereas it can
be challenging if small bleed occurs in extra-axial region particularly in the absence of contrast. Bleed region
needs to be distinguished from bleed-look-alike brain regions which are abnormally bright falx and fresh flowing
blood. We propose an algorithm for detection of brain bleed in various anatomical locations. A preprocessing
step is performed to segment intracranial contents and enhancement of region of interests(ROIs). A number of
bleed and bleed-look-alike candidates are identified from a set of 11 available cases. For each candidate texture
based features are extracted from non-separable quincunx wavelet transform along with some other descriptive
features. The candidates are randomly divided into a training and test set consisting of both bleed and bleed-look-
alike. A supervised classifier is designed based on the training sample features. A performance accuracy of
96% is attained for the independent test candidates.
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