In this paper we propose to develop a computer assisted reading (CAR) tool for ocular disease. This involves
identification and quantitative description of the patterns in retinal vasculature. The features taken into account are
fractal dimension and vessel branching. Subsequently a measure combining all these features are designed which would
help in quantifying the progression of the disease. The aim of the research is to develop algorithms that would help with
parameterization of the eye fundus images, thus improving the diagnostics.
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.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.