Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.
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.
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