2,627 research outputs found
Fully automatic lesion boundary detection in ultrasound breast images
We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of
the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and
in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess
the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued
thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point
of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the
inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding
segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the
initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model).
We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final
boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof-
the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean
technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Stent implant follow-up in intravascular optical coherence tomography images
The objectives of this article are (i) to
utilize computer methods in detection of stent struts
imaged in vivo by optical coherence tomography
(OCT) during percutaneous coronary interventions
(PCI); (ii) to provide measurements for the assessment
and monitoring of in-stent restenosis by OCT post PCI.
Thirty-nine OCT cross-sections from seven pullbacks
from seven patients presenting varying degrees of
neointimal hyperplasia (NIH) are selected, and stent
struts are detected. Stent and lumen boundaries are
reconstructed and one experienced observer analyzed
the strut detection, the lumen and stent area measurements,
as well as the NIH thickness in comparison to
manual tracing using the reviewing software provided
by the OCT manufacturer (LightLab Imaging, MA,
USA). Very good agreements were found between
the computer methods and the expert evaluations
for lumen cross-section area (mean difference =
0.11 ± 0.70 mm2; r2 = 0.98, P\ 0.0001) and the
stent cross-section area (mean difference = 0.10 ±
1.28 mm2; r2 = 0.85, P value\ 0.0001). The average
number of detected struts was 10.4 ± 2.9 per crosssection
when the expert identified 10.5 ± 2.8
(r2 = 0.78, P value\0.0001). For the given patient
dataset: lumen cross-sectional area was on the average
(6.05 ± 1.87 mm2), stent cross-sectional area was
(6.26 ± 1.63 mm2), maximum angle between struts
was on the average (85.96 ± 54.23), maximum,
average, and minimum distance between the stent
and the lumen were (0.18 ± 0.13 mm), (0.08 ±
0.06 mm), and (0.01 ± 0.02 mm), respectively, and
stent eccentricity was (0.80 ± 0.08). Low variability
between the expert and automatic method was
observed in the computations of the most important
parameters assessing the degree of neointimal tissue
growth in stents imaged by OCT pullbacks. After
further extensive validation, the presented methods
might offer a robust automated tool that will improve
the evaluation and follow-up monitoring of in-stent
restenosis in patients
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