Matthew S. Brown, Hyun J. Kim, Gregory H. Chu, Bharath Ramakrishna, Martin Allen-Auerbach, Cheryce P. Fischer, Benjamin Levine, Pawan K. Gupta, Christiaan W. Schiepers, Jonathan G. Goldin
A clinical validation of the bone scan lesion area (BSLA) as a quantitative imaging biomarker was performed in metastatic castration-resistant prostate cancer (mCRPC). BSLA was computed from whole-body bone scintigraphy at baseline and week 12 posttreatment in a cohort of 198 mCRPC subjects (127 treated and 71 placebo) from a clinical trial involving a different drug from the initial biomarker development. BSLA computation involved automated image normalization, lesion segmentation, and summation of the total area of segmented lesions on bone scan AP and PA views as a measure of tumor burden. As a predictive biomarker, treated subjects with baseline BSLA <200 cm2 had longer survival than those with higher BSLA (HR=0.4 and p<0.001). As a surrogate outcome biomarker, subjects were categorized as progressive disease (PD) if the BSLA increased by a prespecified 30% or more from baseline to week 12 and non-PD otherwise. Overall survival rates between PD and non-PD groups were statistically different (HR=0.64 and p=0.007). Subjects without PD at week 12 had longer survival than subjects with PD: median 398 days versus 280 days. BSLA has now been demonstrated to be an early surrogate outcome for overall survival in different prostate cancer drug treatments.
Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.
Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment
and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental
step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation
of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan
is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly
registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account
for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific
intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis
against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of
our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians
yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity
thresholding method. The results show a comparable sensitivity and significantly improved overall specificity,
with a p-value of 0.0069.
Tubes like Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed the
patient and drain contents of the stomach are very commonly used in Intensive Care Units (ICU). The placement of these
tubes is critical for their proper functioning and improper tube placement can even be fatal. Bedside chest radiographs
are considered the quickest and safest method to check the placement of these tubes. Tertiary ICU's typically generate
over 250 chest radiographs per day to confirm tube placement. This paper develops a new fully automatic prototype
computer-aided detection (CAD) system for tube detection on bedside chest radiographs. The core of the CAD system is
the randomized algorithm which selects tubes based on their average repeatability from seed points. The CAD algorithm
is designed as a 5 stage process: Preprocessing (removing borders, histogram equalization, anisotropic filtering),
Anatomy Segmentation (to identify neck, esophagus, abdomen ROI's), Seed Generation, Region Growing and Tube
Selection. The preliminary evaluation was carried out on 64 cases. The prototype CAD system was able to detect ET
tubes with a True Positive Rate of 0.93 and False Positive Rate of 0.02/image and NG tubes with a True Positive Rate of
0.84 and False Positive Rate of 0.02/image respectively. The results from the prototype system show that it is feasible to
automatically detect both tubes on chest radiographs, with the potential to significantly speed the delivery of imaging
services while maintaining high accuracy.
Chest radiographs are the quickest and safest method to check placement of man-made medical devices placed in the
body like catheters, stents and pacemakers etc out of which catheters are the most commonly used devices. The two most
often used catheters especially in the ICU are the Endotracheal (ET) tube used to maintain patient's airway and the
Nasogastric (NG) tube used to feed and administer drugs. Tertiary ICU's typically generate over 250 chest radiographs
per day to confirm tube placement. Incorrect tube placements can cause serious complications and can even be fatal. The
task of identifying these tubes on chest radiographs is difficult for radiologists and ICU personnel given the high volume
of cases. This motivates the need for an automatic detection system to aid radiologists in processing these critical cases
in a timely fashion while maintaining patient safety. To-date there has been very little research in this area. This paper
develops a new fully automatic prototype computer-aided detection (CAD) system for detection and classification of
catheters on chest radiographs using a combination of template matching, morphological processing and region growing.
The preliminary evaluation was carried out on 25 cases. The prototype CAD system was able to detect ET and NG tubes
with sensitivities of 73.7% and 76.5% respectively and with specificities of 91.3% and 84.0% respectively. The results
from the prototype system show that it is feasible to automatically detect both catheters on chest radiographs, with the
potential to significantly speed the delivery of imaging services while maintaining high accuracy.
KEYWORDS: Image compression, Hyperspectral imaging, Signal to noise ratio, Principal component analysis, JPEG2000, 3D image processing, Data compression, Independent component analysis, Chromium, Image processing
Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral image cubes. Unfortunately, some major issues generally encountered in hyperspectral data exploitation at low or very low-bit rate compression, for example, subpixels and mixed pixels which do not occur in traditional pure pixel-based image compression are often overlooked in such a 2D-to-3D compression. Accordingly, a direct application of 2D-to-3D compression techniques to hyperspectral image cubes without taking precaution may result in significant loss of crucial spectral information provided by subtle substances such as small objects, anomalies during low bit-rate lossy compression. This paper takes a rather different view by investigating lossy hyperspectral compression from a perspective of exploring spectral information, referred to as exploitation-based lossy compression and further develops spectral/spatial hyperspectral image compression to effectively preserve crucial and vital spectral information of objects which are generally missed by commonly used mean-squared error (MSE) or signal-to-noise ratio (SNR)-based compression techniques when lossy compression is performed at low bit rates. In order to demonstrate advantages of the proposed spectral/spatial compression approach applications of subpixel target detection and mixed pixel analysis are used for experiments for performance evaluation.
Knee-related injuries including meniscal tears are common in both young athletes and the aging population and require
accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced
skills, confidence in detection of meniscal tears can be quite high. However, for radiologists without musculoskeletal
training, diagnosis of meniscal tears can be challenging. This paper develops a novel computer-aided detection (CAD)
diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an
archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and
specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and
specificity of 77.41% and 81.39%, respectively obtained by experienced radiologists in routine diagnosis without using
the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic
detection of both simple and complex meniscal tears of knees.
Osteoarthritis (OA) is the most common form of arthritis and a major cause of morbidity affecting millions of adults in
the US and world wide. In the knee, OA begins with the degeneration of joint articular cartilage, eventually resulting in
the femur and tibia coming in contact, and leading to severe pain and stiffness. There has been extensive research
examining 3D MR imaging sequences and automatic/semi-automatic techniques for 2D/3D articular cartilage
extraction. However, in routine clinical practice the most popular technique still remain radiographic examination and
qualitative assessment of the joint space. This may be in large part because of a lack of tools that can provide clinically
relevant diagnosis in adjunct (in near real time fashion) with the radiologist and which can serve the needs of the
radiologists and reduce inter-observer variation. Our work aims to fill this void by developing a CAD application that
can generate clinically relevant diagnosis of the articular cartilage damage in near real time fashion. The algorithm
features a 2D Active Shape Model (ASM) for modeling the bone-cartilage interface on all the slices of a Double Echo
Steady State (DESS) MR sequence, followed by measurement of the cartilage thickness from the surface of the bone,
and finally by the identification of regions of abnormal thinness and focal/degenerative lesions. A preliminary
evaluation of CAD tool was carried out on 10 cases taken from the Osteoarthritis Initiative (OAI) database. When
compared with 2 board-certified musculoskeletal radiologists, the automatic CAD application was able to get
segmentation/thickness maps in little over 60 seconds for all of the cases. This observation poses interesting
possibilities for increasing radiologist productivity and confidence, improving patient outcomes, and applying more
sophisticated CAD algorithms to routine orthopedic imaging tasks.
Knee-related injuries involving the meniscal or articular cartilage are common and require accurate diagnosis and
surgical intervention when appropriate. With proper techniques and experience, confidence in detection of meniscal
tears and articular cartilage abnormalities can be quite high. However, for radiologists without musculoskeletal training,
diagnosis of such abnormalities can be challenging. In this paper, the potential of improving diagnosis through
integration of computer-aided detection (CAD) algorithms for automatic detection of meniscal tears and articular
cartilage injuries of the knees is studied. An integrated approach in which the results of algorithms evaluating either
meniscal tears or articular cartilage injuries provide feedback to each other is believed to improve the diagnostic
accuracy of the individual CAD algorithms due to the known association between abnormalities in these distinct
anatomic structures. The correlation between meniscal tears and articular cartilage injuries is exploited to improve the
final diagnostic results of the individual algorithms. Preliminary results from the integrated application are encouraging
and more comprehensive tests are being planned.
Knee-related injuries, including meniscal tears, are common in young athletes and require accurate diagnosis and
appropriate surgical intervention. Although with proper technique and skill, confidence in the detection of meniscal
tears should be high, this task continues to be a challenge for many inexperienced radiologists. The purpose of our study
was to automate detection of meniscal tears of the knee using a computer-aided detection (CAD) algorithm. Automated
segmentation of the sagittal T1-weighted MR imaging sequences of the knee in 28 patients with diagnoses of meniscal
tears was performed using morphologic image processing in a 3-step process including cropping, thresholding, and
application of morphological constraints. After meniscal segmentation, abnormal linear meniscal signal was extracted
through a second thresholding process. The results of this process were validated by comparison with the interpretations
of 2 board-certified musculoskeletal radiologists. The automated meniscal extraction algorithm process was able to
successfully perform region of interest selection, thresholding, and object shape constraint tasks to produce a convex
image isolating the menisci in more than 69% of the 28 cases. A high correlation was also noted between the CAD
algorithm and human observer results in identification of complex meniscal tears. Our initial investigation indicates
considerable promise for automatic detection of simple and complex meniscal tears of the knee using the CAD
algorithm. This observation poses interesting possibilities for increasing radiologist productivity and confidence,
improving patient outcomes, and applying more sophisticated CAD algorithms to orthopedic imaging tasks.
The US Army Joint Service Agent Water Monitor (JSAWM) program is currently interested in an approach that can implement a hardware- designed device in ticket-based hand-held assay (currently being developed) used for chemical/biological agent detection. This paper presents a preliminary investigation of the proof of concept. Three components are envisioned to accomplish the task. One is the ticket development which has been undertaken by the ANP, Inc. Another component is the software development which has been carried out by the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County (UMBC). A third component is an embedded system development which can be used to drive the UMBC-developed software to analyze the ANP-developed HHA tickets on a small pocket-size device like a PDA. The main focus of this paper is to investigate the third component that is viable and is yet to be explored. In order to facilitate to prove the concept, a flatbed scanner is used to replace a ticket reader to serve as an input device. The Stargate processor board is used as the embedded System with Embedded Linux installed. It is connected to an input device such as scanner as well as output devices such as LCD display or laptop etc. It executes the C-Coded processing program developed for this embedded system and outputs its findings on a display device. The embedded system to be developed and investigated in this paper is the core of a future hardware device. Several issues arising in such an embedded system will be addressed. Finally, the proof-of-concept pilot embedded system will be demonstrated.
Hyperspectral image compression can be performed by either 3-D compression or spectral/spatial compression. It has been demonstrated that due to high spectral resolution hyperspectral image compression can be more effective if compression is carried out spectrally and spatially in two separate stages. One commonly used spectral/spatial compression implements principal components analysis (PCA) or wavelet for spectral compression followed by a 2-D/3D compression technique for spatial compression. This paper presents another type of spectral/spatial compression technique, which uses Hyvarinen and Oja's Fast independent component analysis (FastICA) to perform spectral compression, while JPEG2000 is used for 2-D/3-D spatial compression. In order to determine how many independent components are required, a newly developed concept, virtual dimensionality (VD) is used. Since the VD is determined by the false alarm probability rather than the commonly used signal-to-noise ratio or mean squared error (MSE), our proposed FastICA-based spectral/spatial compression is more effective than PCA-based or wavelet-based spectral/spatial compression in data exploitation.
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.