Computer-aided diagnosis (CAD) systems usually require information about regions of interest in images, like:
lungs (for nodule detection), colon (for identifying polyps), etc. Many times, it is computationally intensive to
process large data sets as in CT to find these areas of interest. A fast and accurate recognition of the different
regions of interest in the human body from images is therefore necessary. In this paper we propose a fast and
efficient algorithm that can detect the organs of interest in a CT volume and estimate their sizes. Instead of
analyzing the whole 3D volume; which is computationally expensive, a binary search technique is adapted to
search in a few slices. The slices selected in the search process is segmented and different regions are labeled.
Decision over whether the image belongs to a lung or colon or both is based on the count of lung/colon pixels
in the slice. Once the detection is done we look for the start and end slice of the body part to have an estimate
of their sizes. The algorithm involves certain search decisions based on some predefined threshold values which
are empirically chosen from a training data set. The effectiveness of our technique is confirmed by applying
it on an independent test data set. Detection accuracy of 100% is obtained on a test set. This algorithm can
be integrated in a CAD system for running the right application, or can be used in pre-sets for visualization
purposes and other post-processing like image registration etc.
Chest X-ray (CXR) data is a 2D projection image. The main drawback of such an image is that each pixel
of it represents a volumetric integration. This poses a challenge in detection and estimation of nodules and
their characteristics. Due to human anatomy there are a lot of lung structures which can be falsely identified as
nodules in a projection data. Detection of nodules with a large number of false positives (FP) adds more work
for the radiologists.
With the help of CAD algorithms we aim to identify regions which cause higher FP readings or provide
additional information for nodule detection based on the human anatomy.
Different lung regions have different image characteristics we take advantage of this and propose an automatic
lung partitioning into vessel, apical, basal and exterior pulmonary regions. Anatomical landmarks like aortic
arch and end of cardiac-notch along-with inter intra-rib width and their shape characteristics were used for this
partitioning. Likelihood of FPs is more in vessel, apical and exterior pulmonary regions due to rib-crossing,
overlap of vessel with rib and vessel branching. For each of these three cases, special features were designed
based on histogram of rib slope and the structural properties of rib segments information. These features were
assigned different weights based on the partitioning.
An experiment was carried out using a prototype CAD system 150 routine CXR studies were acquired from
three institutions (24 negatives, rest with one or more nodules). Our algorithm provided a sensitivity of 70.4%
with 5 FP/image for cross-validation without partition. Inclusion of the proposed techniques increases the
sensitivity to 78.1% with 4.1 FP/image.
Chest X-ray (CXR) data is a projection image where each pixel of it represents a volumetric integration. Consequently
identification of nodules and their characteristics is a difficult task in such images.
Using a novel application of random process-based fractal image processing technique we extract features for
nodule characterization. The uniqueness of the proposed technique lies in the fact that instead of relying on
apriori information from user as in other random process inspired measures, we translate the random walk process
into a feature which is based on its realization values. The Normalized Fractional Brownian Motion (NFBM)
Model is derived from the random walk process. Using neighborhood region information in an incremental
manner we can characterize the smoothness or roughness of a surface. The NFBM system gives a measure of
roughness of a surface which in our case is a suspicious region (probable nodule). A classification procedure uses
this measure to categorize nodule and non-nodule structures in the lung.
The NFBM feature set is integrated in a prototype CAD system for nodule detection in CXR. Our algorithm
provided a sensitivity of 75.9% with 3.1 FP/image on an independent test set of 50 CXR studies.
Purpose: To compare sensitivity and reading time when using CAD as second reader resp. concurrent reader.
Materials and Methods: Fifty chest MDCT scans due to clinical indication were analysed independently by four radiologists two times: First with CAD as concurrent reader (display of CAD results simultaneously to the primary reading by the radiologist); then after a median of 14 weeks with CAD as second reader (CAD results were shown after completion of a reading session without CAD). A prototype version of Siemens LungCAD (Siemens,Malvern,USA) was used. Sensitivities and reading times for detecting nodules ≥4mm of concurrent reading, reading without CAD and second reading were recorded. In a consensus conference false positive findings were eliminated. Student's T-Test was used to compare sensitivities and reading times. Results: 108 true positive nodules were found. Mean sensitivity was .68 for reading without CAD, .68 for concurrent reading and .75 for second reading. Differences of sensitivities were significant between concurrent and second reading (p<.001) resp. reading without CAD and second reading (p=.001). Mean reading time for concurrent reading was significant shorter (274s) compared to reading without CAD (294s;p=.04) and second reading (337s;p<.001). New work to be presented: To our knowledge this is the first study that compares sensitivities and reading times between use of CAD as concurrent resp. second reader. Conclusion: CAD can either be used to speed up reading of chest CT cases for pulmonary nodules without loss of sensitivity as concurrent reader -OR (and not AND) to increase sensitivity and reading time as second reader.
The Computed Tomography (CT) modality shows not only the body of the patient in the volumes it generates, but also the clothing, the cushion and the table. This might be a problem especially for two applications. The first is 3D visualization, where the table has high density parts that might hide regions of interest. The second is registration of acquisitions obtained at different time points; indeed, the table and cushions might be visible in one data set only, and their positions and shapes may vary, making the registration less accurate. An automatic approach for extracting the body would solve those problems. It should be robust, reliable, and fast. We therefore propose a multi-scale method based on deformable models. The idea is to move a surface across the image that attaches to the boundaries of the body. We iteratively compute forces which take into account local information around the surface. Those make it move through the table but ensure that it stops when coming close to the body. Our model has elastic properties; moreover, we take into account the fact that some regions in the volume convey more information than others by giving them more weight. This is done by using normalized convolution when regularizing the surface. The algorithm*, tested on a database of over a hundred volumes of
whole body, chest or lower abdomen, has proven to be very efficient, even for volumes with up to 900 slices, providing accurate results in an average time of 6 seconds. It is also robust against noise and variations of scale and table's shape.
A novel method called local shape controlled voting has been developed for spherical object detection in 3D voxel
images. By combining local shape properties into the global tracking procedure of normal overlap, the proposed
method solved the ambiguities of normal overlap between a small size sphere and a possible large size cylinder,
as the normal overlap technique can only measures the 'density' of normal overlapping, while how the normal
vectors are distributed in 3D is not discovered. The proposed method was applied to computer aided detection
of small size pulmonary nodules based on helical CT images. Experiments showed that this method attained a
better performance compared to the original normal overlap technique.
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